To enable easy , rigorous , and repeatable comparison of the capabilities of such systems , we present an approach and associated software for analyzing the performance and scalability of parallel , open-source graph libraries . We demonstrate our approach on five graph frameworks : GraphMat , the Graph500 , the Graph Algorithm Platform , GraphBIG , and PowerGraph using synthetic and real-world datasets . We examine previously overlooked aspects of parallel graph processing performance such as phases of execution and energy usage for three algorithms : breadth first search , single source shortest paths , and PageRank and compare our results to Graphalytics . The development of graphical Markov started with work by Wermuth ( 1976 , 1980 ) and Darroch , Lauritzen and Speed ( 1980 ) which built on early results in 1920 to 1930 by geneticist Sewall Wright and probabilist Andrej Markov as well as on results for log-linear models by Birch ( 1963 ) , Goodman ( 1970 ) , Bishop , Fienberg and Holland ( 1973 ) and for covariance selection by Dempster ( 1972 ) . Wright used graphs , in which nodes represent variables and arrows indicate linear dependence , to describe hypotheses about stepwise processes in single responses that could have generated his data . He developed a method , called path analysis , to estimate linear dependences and to judge whether the hypotheses are well compatible with his data which he summarized in terms of simple and partial correlations . Chapter 6 . Graphs : Flexible Representations of Molecular Structures and Biological Networks ( Milind Misra , Shawn Martin , and Jean-Loup Faulon ) . Chapter 7 . A Chinese semantic treebank was built under the proposed framework , and the first release containing about 14 K sentences is made freely available . The proposed framework enables semantic role labeling to be solved as a sequence labeling task , and experiments show that standard sequence labelers can give competitive performance on the new treebank compared with state-of-the-art graph structure models . It is the most demonstrative way to present the transformation . But applying the graph grammar to the graph of model means to find the subgraph isomorphic to the left part of the grammar rule . This is an NP-complete task . This paper presents an approach to improve time of discrete event simulations . For that , the Parallel Discrete EVent System formalism is coupled to a partitioning method in order to parallelize the graph of models . We will present the graph partitioning method to realize this cutting and quantify the resulting time savings of parallel implementation . This calls for automated offside detection techniques in order to assist accurate refereeing . In this work , we have explicitly used computer vision and image processing techniques like Hough transform , color similarity ( quantization ) , graph connected components , and vanishing point ideas to identify the probable offside regions . Keywords : Hough transform , connected components , KLT tracking , color similarity . Findings The evaluators rated managerial performance significantly lower in the graph format compared to a table presentation of the BSC . Performance ratings were significantly higher for the scenario where the manager failed to meet only financial perspective targets but exceeded targets for all other nonfinancial BSC perspectives , contrary to the usual predictions based on the financial measure bias . The findings indicate that the degree of support provided by a particular graph format for a particular data extraction task depends on the matching of format and task in terms of their anchoring characteristics . Anchoring , in this context , refers to the phenomenon that specific and diverse parts of a graph are segmented by the reader to act as salient and relevant cues , or anchors , when different classes of information are to be extracted from the graph . A final post - pooling network that takes to a parameterization of a diagonal Gaussian . The graphical model for this is given at the right of Figure [ reference ] . We note that the humble sample mean already gives the statistic network a great deal of representational power due to the fact that the instance encoder can learn a representation where averaging makes sense . Newell and Deng presented the associative embedding for grouping joint candidates . Nevertheless , all these approaches partition joints based on partitioning the graph covering the whole image , resulting in high inference complexity . In contrast , GPN performs local inference with robust global affinity cues which is efficiently generated by dense regressions from the centroid embedding space , reducing complexity for joint partitions and improving pose estimation . First , we identify the syntactic structure of the question using a dependency parser . This produces a graph representation of the question in which each node represents a word and each edge a particular type of dependency ( e.g . determiner , nominal subject , direct object , etc . ) . Second , we associate each word ( node ) with a vector embedding pretrained on large corpora of text data . We evaluated variants of our model to measure the impact of various design choices ( see numbered rows in Table [ reference ] ) . On the question side , we evaluate ( row 1 ) our graph approach without syntactic parsing , building question graphs with only two types of edges , previous / next and linking consecutive nodes . This shows the advantage of using the graph method together with syntactic parsing . The best performing model is GCNSeq , both as a tree and as a graph encoder , with the latter obtaining the highest results . Table [ reference ] shows the comparison between our best sequential ( Seq ) , tree ( GCNSeq without reentrancies , henceforth called Tree ) and graph encoders ( GCNSeq with reentrancies , henceforth called Graph ) on the test set of LDC2015E86 and LDC2017T10 . We also include state - of - the - art results reported on these datasets for sequential encoding konstas2017neural and graph encoding song , beck . In this work , we study feature learning techniques for graph - structured inputs . Our starting point is previous work on Graph Neural Networks scarselli2009graph , which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences . The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence - based models ( e.g. , LSTMs ) when the problem is graph - structured . There are two main differences : first , in GNNs the graph structure is explicit , which makes the models less general but may provide stronger generalization ability ; second , pointer networks require that each node has properties ( e.g. , a location in space ) , while GNNs can represent nodes that are defined only by their position in the graph , which makes them more general along a different dimension . GGS - NNs are related to soft alignment and attentional models ( e.g. , ) in two respects : first , the graph representation in ( [ reference ] ) uses context to focus attention on which nodes are important to the current decision ; second , node annotations in the program verification example keep track of which nodes have been explained so far , which gives an explicit mechanism for making sure that each node in the input has been used over the sequence of producing an output . section : Discussion We are particularly interested in continuing to develop end - to - end learnable systems that can learn about semantic properties of programs , that can learn more complicated graph algorithms , and in applying these ideas to problems that require reasoning over knowledge bases and databases . More generally , we consider these graph neural networks as representing a step towards a model that can combine structured representations with the powerful algorithms of deep learning , with the aim of taking advantage of known structure while learning and inferring how to reason with and extend these representations . section : Acknowledgements Relational networks , interaction networks and our proposed model can all be seen as an instance of Graph Neural Networks scarselli2009graph , gilmer2017neural . Graph neural networks with message passing computations go back to . However , there are key differences that we found important for implementing stable multi - step relational reasoning . In these instances , one has to solve two problems : ( i ) Determining the node sequences for which neighborhood graphs are created and ( ii ) computing a normalization of neighborhood graphs , that is , a unique mapping from a graph representation into a vector space representation . The proposed approach , termed Patchy - san , addresses these two problems for arbitrary graphs . Examples in this family include and . Finally define a message passing process on graphs which is run until convergence , instead of for a finite number of time steps as in MPNNs . section : QM9 Dataset There have been some previous work studying graph CNN on more general graphs instead of 2D regular grids , and have a special focus on near - isometric 3D shape graphs like human bodies . To generalize image CNN , These work usually tries to tackle the following three challenges : defining translation structures on graphs to allow parameter sharing ; designing compactly supported filters on graphs ; aggregating multi - scale information . Their constructions of deep neural network usually fall into two types : spatial construction and spectral construction . For the wide component , we encode a variety of discourse relations between sentences . A Graph Convolutional Network ( GCN ) is then applied over the sentence graph to generate high-level relational representations for headline generation . The whole model could be optimized end-to-end so that the structure and representation could be learned jointly . Inspired by the successful application of graph convolutional network in node classification and graph classification , we propose a novel malware similarity metric method based on graph convolutional network . We use graph convolutional network to compute the graph embedding vectors , and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors . Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method , and the accuracy of clustering application with our method reaches to 97 % with high time efficiency . We take the Documentary Hypothesis , a prominent theory regarding the composition of the first five books of the Hebrew bible , extract stylistic features designed to avoid bias or overfitting , and train several classification models . Our main result is that the recently-introduced graph convolutional network architecture outperforms structurallyuninformed models . We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers , in contrast to simple logistic regression . The questions in YouQuek are not limited to cues on one frame but related to logical reasoning in the temporal dimension . Observing the lack of effective representations for modeling long videos , we propose a set of carefully designed models including a novel Recurrent Graph Convolutional Network ( RGCN ) that captures both temporal order and relation information . Furthermore , we study multiple modalities including description and transcripts for the purpose of boosting video understanding . Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution . Gender bias has been found in existing coreference resolvers . Specifcally , we frst use a Dual Convolutional Neural Network ( Dual-CNN ) to obtain patient representations based on EHRs . Then , we introduce the medicine knowledge associated with predicted medicines to create a dynamic medicine knowledge graph , and use a Relational Graph Convolutional Network ( R-GCN ) to encode it . Finally , CompNet selects medicines by fusing the combination of patient information and the medicine knowledge graph . To evaluate our method , we collect an Esophageal Cancer Radiotherapy Target Treatment Contouring dataset of 81 patients which includes tomography images with radiotherapy target . On this dataset , our convolutional graph network produces state-of-the-art results and outperforms the baselines . With the addition of interactive setting , performance is improved even further . Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks : people are related in myriad ways , but most graph learning models treat relations as binary . In this paper , we develop a graph-based convolutional network for learning on multi-view networks . We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries . First , we use visual heuristics to augment relationships between pairs of objects . Second , we introduce a graph convolution-based network to generate a scene graph context representation that enriches the image generation . These contributions significantly improve the scene composition ( relation score of 59.8 % compared to 51.2 % ) and image generation ( 74 % versus 64 % in mean relation opinion score ) . document : Semi - Supervised Classification with Graph Convolutional Networks We present a scalable approach for semi - supervised learning on graph - structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs . Our code and data are available at . Graph2Seq is simple yet general and is highly extensible where its two building blocks , graph encoder and sequence decoder , can be replaced by other models such as Graph Convolutional ( Attention ) Networks kipf2016semi , velickovic2017graph or their extensions schlichtkrull2017modeling , and LSTM hochreiter1997long . li2015gated by introducing modern practices of RNN ( using of GRU updates ) in the original GNN framework . Another important stream of work that has recently drawn fast increasing interest is graph convolutional networks ( GCN ) built on spectral graph theory , introduced by and then extended by with fast localized convolution . Most of these approaches can not scale to large graphs , which is improved by using a localized first - order approximation of spectral graph convolution kipf2016semi and further equipping with important sampling for deriving a fast GCN chen2018fastgcn . Graph Convolutional Neural Networks ( GCNNs ) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi - cross - domains including bioinformatics , chemoinformatics , social networks , natural language processing and computer vision . In this paper , we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in and propose our Graph Capsule Network ( GCAPS - CNN ) model . It is inspired by the notion of capsules developed in : capsules are new types of neurons which encapsulate more information in a local pool operation ( e.g. , a convolution operation in a CNN ) by computing a small vector of highly informative outputs rather than just taking a scalar output . Our graph capsule idea is quite general and can be employed in any version of GCNN model either design for solving graph semi - supervised problem or doing sequence learning on graphs via Graph Convolution Recurrent Neural Network models ( GCRNNs ) . The first limitation of the standard GCNN model is due to the basic graph convolution operation which is defined – in its purest form – as the aggregation of node values in a local neighborhood corresponding to each feature ( or channel ) . Limited by the nature of deep neural network , previous methods usually represent a 3D shape in volume or point cloud , and it is non - trivial to convert them to the more ready - to - use mesh model . Unlike the existing methods , our network represents 3D mesh in a graph - based convolutional neural network and produces correct geometry by progressively deforming an ellipsoid , leveraging perceptual features extracted from the input image . We adopt a coarse - to - fine strategy to make the whole deformation procedure stable , and define various of mesh related losses to capture properties of different levels to guarantee visually appealing and physically accurate 3D geometry . It requires the integration of the knowledge learned from two data modalities . On the 3D geometry side , we directly build a graph based fully convolutional network ( GCN ) on the mesh model , where the vertices and edges in the mesh are directly represented as nodes and connections in a graph . Network feature encoding information for 3D shape is saved on each vertex . The image feature network is a 2D CNN that extract perceptual feature from the input image , which is leveraged by the mesh deformation network to progressively deform an ellipsoid mesh into the desired 3D model . The cascaded mesh deformation network is a graph - based convolution network ( GCN ) , which contains three deformation blocks intersected by two graph unpooling layers . Each deformation block takes an input graph representing the current mesh model with the 3D shape feature attached on vertices , and produces new vertices locations and features . subsubsection : G - ResNet After obtaining 1408 - dim feature for each vertex representing both 3D shape and 2D image information , we design a graph based convolutional neural network to predict new location and 3D shape feature for each vertex . This requires efficient exchange of the information between vertices . [ reference ] , the iterative classification algorithm ( ICA ) [ reference ] and Planetoid [ reference ] . We also directly compare our model against GCNs [ reference ] , as well as graph convolutional models utilising higher - order Chebyshev filters [ reference ] , and the MoNet model presented in [ reference ] . Inductive learning For the inductive learning task , we compare against the four different supervised GraphSAGE inductive methods presented in [ reference ] . These provide a variety of approaches to aggregating features within a sampled neighborhood : GraphSAGE - GCN ( which extends a graph convolution - style operation to the inductive setting ) , GraphSAGE - mean ( taking the elementwise mean value of feature vectors ) , GraphSAGE - LSTM ( aggregating by feeding the neighborhood features into an LSTM ) and GraphSAGE - pool ( taking the elementwise maximization operation of feature vectors transformed by a shared nonlinear multilayer perceptron ) . ComplEx extends DistMult to the complex domain . A somewhat separate line of link prediction research introduces Relational Graph Convolutional Networks ( R - GCNs ) . R - GCNs use a convolution operator to capture locality information in graphs . Our research involves each of these steps as they relate to Sowa ’s conceptual structures and Wille ’s concept lattices . Formal Concept Analysis gives us a capability to perform concept formation via symbolic machine learning . Concept(ual ) Graphs provide a means to describe relational properties between primitive concept and relation types . We describe a case study in the application of symbolic machine learning techniques for the discovery of linguistic rules and categories . A supervised rule induction algorithm is used to learn to predict the correct diminutive suffix given the phonological representation of Dutch nouns . The purpose of this work is to analyze the feasibility of implementing the Data Mining process on data provided by PRF in order to identify associations between variables related to transit accidents in all Brazilian federal highways . Method : It was used symbolic supervised learning algorithms , as well as an algorithm of generation of association rules , implemented in Weka tool . Regarding the database , it was used the records of 2012 . We show that the methodology currently in use for comparing symbolic supervised learning methods applied to human language technology tasks is unreliable . We show that the interaction between algorithm parameter settings and feature selection within a single algorithm often accounts for a higher variation in results than differences between different algorithms or information sources . Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better . In this paper , we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning ( SDRL ) that can handle both high-dimensional sensory inputs and symbolic planning . The task-level interpretability is enabled by relating symbolic actions to options . We are in fact convinced that the study of well-established cognitive models of human learning can suggest new , interesting approaches to problem so far not satisfactorily solved in the field of machine learning . Further , we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience . The model was implemented and tested in some simple experimental settings , with reference to the task of learning sensorimotor sequences . A Symbolic Learning Scheme for a Connectionist Associative Memory . This paper deals with the integration of neural and symbolic approaches . The first is to introduce and define protein motifs , and provide a survey of previous research on protein motif discovery . The second is to present and apply a novel approach to protein motif representation and discovery , which is based on aspatial description logic and the symbolic machine learning paradigm of structured concept formation . A large database of protein fragments is processed using this approach , and several interesting and significant protein motifs are discovered . A single pass through the data suffices to evaluate multisplits of all arities . This class contains many important attribute evaluation functions familiar from symbolic machine learning research . Our empirical experiments convey that there is no significant differences in efficiency between the method that produces optimal partitions and those that are based on heuristics . This paper is concerned with personalisation of user agents by symbolic , on-line machine learning techniques . The application of these ideas to an infotainment agent is discussed in detail . Deep Symbolic Reinforcement Learning ( DSRL ) seeks to incorporate such capacities to deep Q-networks ( DQN ) by learning a relevant symbolic representation prior to using Q-learning . In this paper , we propose a novel extension of DSRL , which we call Symbolic Reinforcement Learning with Common Sense ( SRL+CS ) , offering a better balance between generalization and specialization , inspired by principles of common sense when assigning rewards and aggregating Q-values . Experiments reported in this paper show that SRL+CS learns consistently faster than Q-learning and DSRL , achieving also a higher accuracy . One of its main attributes is the generation of hybrid models of 3D objects , integrating implicit representations by neural networks and explicit descriptions by semantic networks . On both levels of the vision system , subsymbolic neural learning as well as symbolic semantic learning can be done completely unsupervised after defining a few constraints only . This allows us to adapt our vision system to new objects and domains without intensive training phases and without " handcrafting " object models by an expert . Experiments show that TransGate not only outperforms state-of-art baselines , but also reduces parameters greatly . For example , TransGate outperforms ConvE and RGCN with 6x and 17x fewer parameters , respectively . These results indicate that parameter sharing is a superior way to further optimize embedding and TransGate finds a better trade-off between complexity and expressivity . How to enhance the robustness of GCNs remains a critical open problem . To address this problem , we propose Robust GCN ( RGCN ) , a novel model that " fortifies ' ' GCNs against adversarial attacks . Specifically , instead of representing nodes as vectors , our method adopts Gaussian distributions as the hidden representations of nodes in each convolutional layer . Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure , and prevalent approaches among them can not effectively use the syntactic information . In this paper , we propose to use a novel framework named as recurrent graph convolutional network ( RGCN ) for target-dependent sentiment classification in which the given text is considered as a graph based on its syntactic structure and recurrent graph convolutional networks are used to encode the text and target . We conduct comprehensive experiments on publicly accessible datasets , and results demonstrate that our model outperforms the state-of-the-art baselines . The questions in YouQuek are not limited to cues on one frame but related to logical reasoning in the temporal dimension . Observing the lack of effective representations for modeling long videos , we propose a set of carefully designed models including a novel Recurrent Graph Convolutional Network ( RGCN ) that captures both temporal order and relation information . Furthermore , we study multiple modalities including description and transcripts for the purpose of boosting video understanding . In this paper , we propose a new graph-based framework , which is termed as recurrent graph convolutional network based multi-task TSA ( RGCN-MT-TSA ) . Both the graph convolutional network ( GCN ) and the long short-term memory ( LSTM ) unit are aggregated to form the recurrent graph convolutional network ( RGCN ) , where the GCN explicitly integrate the bus ( node ) states with the topological characteristics while the LSTM subsequently captures the temporal features . We also propose a multi-task learning ( MTL ) scheme , which provides joint training of stability classification ( Task-1 ) as well as critical generator identification ( Task-2 ) in the framework , and accelerate the process with parallel computing . As a supplement , the in silico method can provide helpful information for predictions of DTIs in a timely manner . In this work , a deep walk embedding method is developed for predicting DTIs from a multi-molecular network . More specifically , a multi-molecular network , also called molecular associations network , is constructed by integrating the associations among drug , protein , disease , lncRNA , and miRNA . BackgroundThe graph-theoretical analysis of molecular networks has a long tradition in chemoinformatics . As demonstrated frequently , a well designed format to encode chemical structures and structure-related information of organic compounds is the Molfile format . Recent technological advances in mass spectrometry-based phosphoproteomics have enabled us to measure network-wide signaling dynamics in a comprehensive and quantitative manner . As conventional protein-protein interaction ( PPI ) information-based network analysis is insufficient to systematically analyze phosphorylation site-dependent complex interaction dynamics , here we develop and evaluate a platform to provide a high-resolution molecular network description for kinase-substrate interactome analysis . RESULTS In this study , we developed a Cytoscape-based bioinformatical platform named ' Post Translational Modification mapper ( PTMapper ) ' to integrate PPI data with publicly available kinase-substrate relations at the resolution of phosphorylated amino acid residues . One category of relaxation oscillators is given by astable multivibrators , that include a bistable component at the core of their architecture . Here we describe a molecular network motif that operates as an astable multivibrator , and relies on a bistable switch ( the classical Gardner and Collins genetic switch ) that is toggled between its stable steady states by a persistent input . We show that oscillations arise in the presence of two negative feedback loops that process the persistent input and influence the production of the molecular species forming the bistable subsystem . Regulatory and signalling pathways involve DNA , RNA , proteins and metabolites as key elements to coordinate most aspects of cellular functioning . Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions . This chapter describes several types of biological networks , how combination of different analytic approaches can be used to study diseases , and provides a list of selected tools for network visualization and analysis .
To enable easy , rigorous , and repeatable comparison of the capabilities of such systems , we present an approach and associated software for analyzing the performance and scalability of parallel , open-source graph libraries . We demonstrate our approach on five graph frameworks : GraphMat , the Graph500 , the Graph Algorithm Platform , GraphBIG , and PowerGraph using synthetic and real-world datasets . We examine previously overlooked aspects of parallel graph processing performance such as phases of execution and energy usage for three algorithms : breadth first search , single source shortest paths , and PageRank and compare our results to Graphalytics .
The development of graphical Markov started with work by Wermuth ( 1976 , 1980 ) and Darroch , Lauritzen and Speed ( 1980 ) which built on early results in 1920 to 1930 by geneticist Sewall Wright and probabilist Andrej Markov as well as on results for log-linear models by Birch ( 1963 ) , Goodman ( 1970 ) , Bishop , Fienberg and Holland ( 1973 ) and for covariance selection by Dempster ( 1972 ) . Wright used graphs , in which nodes represent variables and arrows indicate linear dependence , to describe hypotheses about stepwise processes in single responses that could have generated his data . He developed a method , called path analysis , to estimate linear dependences and to judge whether the hypotheses are well compatible with his data which he summarized in terms of simple and partial correlations .
Chapter 6 . Graphs : Flexible Representations of Molecular Structures and Biological Networks ( Milind Misra , Shawn Martin , and Jean-Loup Faulon ) . Chapter 7 .
A Chinese semantic treebank was built under the proposed framework , and the first release containing about 14 K sentences is made freely available . The proposed framework enables semantic role labeling to be solved as a sequence labeling task , and experiments show that standard sequence labelers can give competitive performance on the new treebank compared with state-of-the-art graph structure models .
It is the most demonstrative way to present the transformation . But applying the graph grammar to the graph of model means to find the subgraph isomorphic to the left part of the grammar rule . This is an NP-complete task .
This paper presents an approach to improve time of discrete event simulations . For that , the Parallel Discrete EVent System formalism is coupled to a partitioning method in order to parallelize the graph of models . We will present the graph partitioning method to realize this cutting and quantify the resulting time savings of parallel implementation .
This calls for automated offside detection techniques in order to assist accurate refereeing . In this work , we have explicitly used computer vision and image processing techniques like Hough transform , color similarity ( quantization ) , graph connected components , and vanishing point ideas to identify the probable offside regions . Keywords : Hough transform , connected components , KLT tracking , color similarity .
Findings The evaluators rated managerial performance significantly lower in the graph format compared to a table presentation of the BSC . Performance ratings were significantly higher for the scenario where the manager failed to meet only financial perspective targets but exceeded targets for all other nonfinancial BSC perspectives , contrary to the usual predictions based on the financial measure bias .
The findings indicate that the degree of support provided by a particular graph format for a particular data extraction task depends on the matching of format and task in terms of their anchoring characteristics . Anchoring , in this context , refers to the phenomenon that specific and diverse parts of a graph are segmented by the reader to act as salient and relevant cues , or anchors , when different classes of information are to be extracted from the graph .
A final post - pooling network that takes to a parameterization of a diagonal Gaussian . The graphical model for this is given at the right of Figure [ reference ] . We note that the humble sample mean already gives the statistic network a great deal of representational power due to the fact that the instance encoder can learn a representation where averaging makes sense .
Newell and Deng presented the associative embedding for grouping joint candidates . Nevertheless , all these approaches partition joints based on partitioning the graph covering the whole image , resulting in high inference complexity . In contrast , GPN performs local inference with robust global affinity cues which is efficiently generated by dense regressions from the centroid embedding space , reducing complexity for joint partitions and improving pose estimation .
First , we identify the syntactic structure of the question using a dependency parser . This produces a graph representation of the question in which each node represents a word and each edge a particular type of dependency ( e.g . determiner , nominal subject , direct object , etc . ) . Second , we associate each word ( node ) with a vector embedding pretrained on large corpora of text data .
We evaluated variants of our model to measure the impact of various design choices ( see numbered rows in Table [ reference ] ) . On the question side , we evaluate ( row 1 ) our graph approach without syntactic parsing , building question graphs with only two types of edges , previous / next and linking consecutive nodes . This shows the advantage of using the graph method together with syntactic parsing .
The best performing model is GCNSeq , both as a tree and as a graph encoder , with the latter obtaining the highest results . Table [ reference ] shows the comparison between our best sequential ( Seq ) , tree ( GCNSeq without reentrancies , henceforth called Tree ) and graph encoders ( GCNSeq with reentrancies , henceforth called Graph ) on the test set of LDC2015E86 and LDC2017T10 . We also include state - of - the - art results reported on these datasets for sequential encoding konstas2017neural and graph encoding song , beck .
In this work , we study feature learning techniques for graph - structured inputs . Our starting point is previous work on Graph Neural Networks scarselli2009graph , which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences . The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence - based models ( e.g. , LSTMs ) when the problem is graph - structured .
There are two main differences : first , in GNNs the graph structure is explicit , which makes the models less general but may provide stronger generalization ability ; second , pointer networks require that each node has properties ( e.g. , a location in space ) , while GNNs can represent nodes that are defined only by their position in the graph , which makes them more general along a different dimension . GGS - NNs are related to soft alignment and attentional models ( e.g. , ) in two respects : first , the graph representation in ( [ reference ] ) uses context to focus attention on which nodes are important to the current decision ; second , node annotations in the program verification example keep track of which nodes have been explained so far , which gives an explicit mechanism for making sure that each node in the input has been used over the sequence of producing an output . section : Discussion
We are particularly interested in continuing to develop end - to - end learnable systems that can learn about semantic properties of programs , that can learn more complicated graph algorithms , and in applying these ideas to problems that require reasoning over knowledge bases and databases . More generally , we consider these graph neural networks as representing a step towards a model that can combine structured representations with the powerful algorithms of deep learning , with the aim of taking advantage of known structure while learning and inferring how to reason with and extend these representations . section : Acknowledgements
Relational networks , interaction networks and our proposed model can all be seen as an instance of Graph Neural Networks scarselli2009graph , gilmer2017neural . Graph neural networks with message passing computations go back to . However , there are key differences that we found important for implementing stable multi - step relational reasoning .
In these instances , one has to solve two problems : ( i ) Determining the node sequences for which neighborhood graphs are created and ( ii ) computing a normalization of neighborhood graphs , that is , a unique mapping from a graph representation into a vector space representation . The proposed approach , termed Patchy - san , addresses these two problems for arbitrary graphs .
Examples in this family include and . Finally define a message passing process on graphs which is run until convergence , instead of for a finite number of time steps as in MPNNs . section : QM9 Dataset
There have been some previous work studying graph CNN on more general graphs instead of 2D regular grids , and have a special focus on near - isometric 3D shape graphs like human bodies . To generalize image CNN , These work usually tries to tackle the following three challenges : defining translation structures on graphs to allow parameter sharing ; designing compactly supported filters on graphs ; aggregating multi - scale information . Their constructions of deep neural network usually fall into two types : spatial construction and spectral construction .
For the wide component , we encode a variety of discourse relations between sentences . A Graph Convolutional Network ( GCN ) is then applied over the sentence graph to generate high-level relational representations for headline generation . The whole model could be optimized end-to-end so that the structure and representation could be learned jointly .
Inspired by the successful application of graph convolutional network in node classification and graph classification , we propose a novel malware similarity metric method based on graph convolutional network . We use graph convolutional network to compute the graph embedding vectors , and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors . Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method , and the accuracy of clustering application with our method reaches to 97 % with high time efficiency .
We take the Documentary Hypothesis , a prominent theory regarding the composition of the first five books of the Hebrew bible , extract stylistic features designed to avoid bias or overfitting , and train several classification models . Our main result is that the recently-introduced graph convolutional network architecture outperforms structurallyuninformed models . We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers , in contrast to simple logistic regression .
The questions in YouQuek are not limited to cues on one frame but related to logical reasoning in the temporal dimension . Observing the lack of effective representations for modeling long videos , we propose a set of carefully designed models including a novel Recurrent Graph Convolutional Network ( RGCN ) that captures both temporal order and relation information . Furthermore , we study multiple modalities including description and transcripts for the purpose of boosting video understanding .
Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution . Gender bias has been found in existing coreference resolvers .
Specifcally , we frst use a Dual Convolutional Neural Network ( Dual-CNN ) to obtain patient representations based on EHRs . Then , we introduce the medicine knowledge associated with predicted medicines to create a dynamic medicine knowledge graph , and use a Relational Graph Convolutional Network ( R-GCN ) to encode it . Finally , CompNet selects medicines by fusing the combination of patient information and the medicine knowledge graph .
To evaluate our method , we collect an Esophageal Cancer Radiotherapy Target Treatment Contouring dataset of 81 patients which includes tomography images with radiotherapy target . On this dataset , our convolutional graph network produces state-of-the-art results and outperforms the baselines . With the addition of interactive setting , performance is improved even further .
Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks : people are related in myriad ways , but most graph learning models treat relations as binary . In this paper , we develop a graph-based convolutional network for learning on multi-view networks . We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries .
First , we use visual heuristics to augment relationships between pairs of objects . Second , we introduce a graph convolution-based network to generate a scene graph context representation that enriches the image generation . These contributions significantly improve the scene composition ( relation score of 59.8 % compared to 51.2 % ) and image generation ( 74 % versus 64 % in mean relation opinion score ) .
document : Semi - Supervised Classification with Graph Convolutional Networks We present a scalable approach for semi - supervised learning on graph - structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs .
Our code and data are available at . Graph2Seq is simple yet general and is highly extensible where its two building blocks , graph encoder and sequence decoder , can be replaced by other models such as Graph Convolutional ( Attention ) Networks kipf2016semi , velickovic2017graph or their extensions schlichtkrull2017modeling , and LSTM hochreiter1997long .
li2015gated by introducing modern practices of RNN ( using of GRU updates ) in the original GNN framework . Another important stream of work that has recently drawn fast increasing interest is graph convolutional networks ( GCN ) built on spectral graph theory , introduced by and then extended by with fast localized convolution . Most of these approaches can not scale to large graphs , which is improved by using a localized first - order approximation of spectral graph convolution kipf2016semi and further equipping with important sampling for deriving a fast GCN chen2018fastgcn .
Graph Convolutional Neural Networks ( GCNNs ) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi - cross - domains including bioinformatics , chemoinformatics , social networks , natural language processing and computer vision . In this paper , we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in and propose our Graph Capsule Network ( GCAPS - CNN ) model .
It is inspired by the notion of capsules developed in : capsules are new types of neurons which encapsulate more information in a local pool operation ( e.g. , a convolution operation in a CNN ) by computing a small vector of highly informative outputs rather than just taking a scalar output . Our graph capsule idea is quite general and can be employed in any version of GCNN model either design for solving graph semi - supervised problem or doing sequence learning on graphs via Graph Convolution Recurrent Neural Network models ( GCRNNs ) . The first limitation of the standard GCNN model is due to the basic graph convolution operation which is defined – in its purest form – as the aggregation of node values in a local neighborhood corresponding to each feature ( or channel ) .
Limited by the nature of deep neural network , previous methods usually represent a 3D shape in volume or point cloud , and it is non - trivial to convert them to the more ready - to - use mesh model . Unlike the existing methods , our network represents 3D mesh in a graph - based convolutional neural network and produces correct geometry by progressively deforming an ellipsoid , leveraging perceptual features extracted from the input image . We adopt a coarse - to - fine strategy to make the whole deformation procedure stable , and define various of mesh related losses to capture properties of different levels to guarantee visually appealing and physically accurate 3D geometry .
It requires the integration of the knowledge learned from two data modalities . On the 3D geometry side , we directly build a graph based fully convolutional network ( GCN ) on the mesh model , where the vertices and edges in the mesh are directly represented as nodes and connections in a graph . Network feature encoding information for 3D shape is saved on each vertex .
The image feature network is a 2D CNN that extract perceptual feature from the input image , which is leveraged by the mesh deformation network to progressively deform an ellipsoid mesh into the desired 3D model . The cascaded mesh deformation network is a graph - based convolution network ( GCN ) , which contains three deformation blocks intersected by two graph unpooling layers . Each deformation block takes an input graph representing the current mesh model with the 3D shape feature attached on vertices , and produces new vertices locations and features .
subsubsection : G - ResNet After obtaining 1408 - dim feature for each vertex representing both 3D shape and 2D image information , we design a graph based convolutional neural network to predict new location and 3D shape feature for each vertex . This requires efficient exchange of the information between vertices .
[ reference ] , the iterative classification algorithm ( ICA ) [ reference ] and Planetoid [ reference ] . We also directly compare our model against GCNs [ reference ] , as well as graph convolutional models utilising higher - order Chebyshev filters [ reference ] , and the MoNet model presented in [ reference ] . Inductive learning For the inductive learning task , we compare against the four different supervised GraphSAGE inductive methods presented in [ reference ] . These provide a variety of approaches to aggregating features within a sampled neighborhood : GraphSAGE - GCN ( which extends a graph convolution - style operation to the inductive setting ) , GraphSAGE - mean ( taking the elementwise mean value of feature vectors ) , GraphSAGE - LSTM ( aggregating by feeding the neighborhood features into an LSTM ) and GraphSAGE - pool ( taking the elementwise maximization operation of feature vectors transformed by a shared nonlinear multilayer perceptron ) .
ComplEx extends DistMult to the complex domain . A somewhat separate line of link prediction research introduces Relational Graph Convolutional Networks ( R - GCNs ) . R - GCNs use a convolution operator to capture locality information in graphs .
Our research involves each of these steps as they relate to Sowa ’s conceptual structures and Wille ’s concept lattices . Formal Concept Analysis gives us a capability to perform concept formation via symbolic machine learning . Concept(ual ) Graphs provide a means to describe relational properties between primitive concept and relation types .
We describe a case study in the application of symbolic machine learning techniques for the discovery of linguistic rules and categories . A supervised rule induction algorithm is used to learn to predict the correct diminutive suffix given the phonological representation of Dutch nouns .
The purpose of this work is to analyze the feasibility of implementing the Data Mining process on data provided by PRF in order to identify associations between variables related to transit accidents in all Brazilian federal highways . Method : It was used symbolic supervised learning algorithms , as well as an algorithm of generation of association rules , implemented in Weka tool . Regarding the database , it was used the records of 2012 .
We show that the methodology currently in use for comparing symbolic supervised learning methods applied to human language technology tasks is unreliable . We show that the interaction between algorithm parameter settings and feature selection within a single algorithm often accounts for a higher variation in results than differences between different algorithms or information sources .
Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better . In this paper , we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning ( SDRL ) that can handle both high-dimensional sensory inputs and symbolic planning . The task-level interpretability is enabled by relating symbolic actions to options .
We are in fact convinced that the study of well-established cognitive models of human learning can suggest new , interesting approaches to problem so far not satisfactorily solved in the field of machine learning . Further , we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience . The model was implemented and tested in some simple experimental settings , with reference to the task of learning sensorimotor sequences .
A Symbolic Learning Scheme for a Connectionist Associative Memory . This paper deals with the integration of neural and symbolic approaches .
The first is to introduce and define protein motifs , and provide a survey of previous research on protein motif discovery . The second is to present and apply a novel approach to protein motif representation and discovery , which is based on aspatial description logic and the symbolic machine learning paradigm of structured concept formation . A large database of protein fragments is processed using this approach , and several interesting and significant protein motifs are discovered .
A single pass through the data suffices to evaluate multisplits of all arities . This class contains many important attribute evaluation functions familiar from symbolic machine learning research . Our empirical experiments convey that there is no significant differences in efficiency between the method that produces optimal partitions and those that are based on heuristics .
This paper is concerned with personalisation of user agents by symbolic , on-line machine learning techniques . The application of these ideas to an infotainment agent is discussed in detail .
Deep Symbolic Reinforcement Learning ( DSRL ) seeks to incorporate such capacities to deep Q-networks ( DQN ) by learning a relevant symbolic representation prior to using Q-learning . In this paper , we propose a novel extension of DSRL , which we call Symbolic Reinforcement Learning with Common Sense ( SRL+CS ) , offering a better balance between generalization and specialization , inspired by principles of common sense when assigning rewards and aggregating Q-values . Experiments reported in this paper show that SRL+CS learns consistently faster than Q-learning and DSRL , achieving also a higher accuracy .
One of its main attributes is the generation of hybrid models of 3D objects , integrating implicit representations by neural networks and explicit descriptions by semantic networks . On both levels of the vision system , subsymbolic neural learning as well as symbolic semantic learning can be done completely unsupervised after defining a few constraints only . This allows us to adapt our vision system to new objects and domains without intensive training phases and without " handcrafting " object models by an expert .
Experiments show that TransGate not only outperforms state-of-art baselines , but also reduces parameters greatly . For example , TransGate outperforms ConvE and RGCN with 6x and 17x fewer parameters , respectively . These results indicate that parameter sharing is a superior way to further optimize embedding and TransGate finds a better trade-off between complexity and expressivity .
How to enhance the robustness of GCNs remains a critical open problem . To address this problem , we propose Robust GCN ( RGCN ) , a novel model that " fortifies ' ' GCNs against adversarial attacks . Specifically , instead of representing nodes as vectors , our method adopts Gaussian distributions as the hidden representations of nodes in each convolutional layer .
Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure , and prevalent approaches among them can not effectively use the syntactic information . In this paper , we propose to use a novel framework named as recurrent graph convolutional network ( RGCN ) for target-dependent sentiment classification in which the given text is considered as a graph based on its syntactic structure and recurrent graph convolutional networks are used to encode the text and target . We conduct comprehensive experiments on publicly accessible datasets , and results demonstrate that our model outperforms the state-of-the-art baselines .
The questions in YouQuek are not limited to cues on one frame but related to logical reasoning in the temporal dimension . Observing the lack of effective representations for modeling long videos , we propose a set of carefully designed models including a novel Recurrent Graph Convolutional Network ( RGCN ) that captures both temporal order and relation information . Furthermore , we study multiple modalities including description and transcripts for the purpose of boosting video understanding .
In this paper , we propose a new graph-based framework , which is termed as recurrent graph convolutional network based multi-task TSA ( RGCN-MT-TSA ) . Both the graph convolutional network ( GCN ) and the long short-term memory ( LSTM ) unit are aggregated to form the recurrent graph convolutional network ( RGCN ) , where the GCN explicitly integrate the bus ( node ) states with the topological characteristics while the LSTM subsequently captures the temporal features . We also propose a multi-task learning ( MTL ) scheme , which provides joint training of stability classification ( Task-1 ) as well as critical generator identification ( Task-2 ) in the framework , and accelerate the process with parallel computing .
As a supplement , the in silico method can provide helpful information for predictions of DTIs in a timely manner . In this work , a deep walk embedding method is developed for predicting DTIs from a multi-molecular network . More specifically , a multi-molecular network , also called molecular associations network , is constructed by integrating the associations among drug , protein , disease , lncRNA , and miRNA .
BackgroundThe graph-theoretical analysis of molecular networks has a long tradition in chemoinformatics . As demonstrated frequently , a well designed format to encode chemical structures and structure-related information of organic compounds is the Molfile format .
Recent technological advances in mass spectrometry-based phosphoproteomics have enabled us to measure network-wide signaling dynamics in a comprehensive and quantitative manner . As conventional protein-protein interaction ( PPI ) information-based network analysis is insufficient to systematically analyze phosphorylation site-dependent complex interaction dynamics , here we develop and evaluate a platform to provide a high-resolution molecular network description for kinase-substrate interactome analysis . RESULTS In this study , we developed a Cytoscape-based bioinformatical platform named ' Post Translational Modification mapper ( PTMapper ) ' to integrate PPI data with publicly available kinase-substrate relations at the resolution of phosphorylated amino acid residues .
One category of relaxation oscillators is given by astable multivibrators , that include a bistable component at the core of their architecture . Here we describe a molecular network motif that operates as an astable multivibrator , and relies on a bistable switch ( the classical Gardner and Collins genetic switch ) that is toggled between its stable steady states by a persistent input . We show that oscillations arise in the presence of two negative feedback loops that process the persistent input and influence the production of the molecular species forming the bistable subsystem .
Regulatory and signalling pathways involve DNA , RNA , proteins and metabolites as key elements to coordinate most aspects of cellular functioning . Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions . This chapter describes several types of biological networks , how combination of different analytic approaches can be used to study diseases , and provides a list of selected tools for network visualization and analysis .
A Comparison of Parallel Graph Processing Benchmarks Graphical Markov Models Computational approaches in cheminformatics and bioinformatics Dependency Tree Representations of Predicate-Argument Structures An Approach to Graph Matching in the Component of Model Transformations Optimization of Parallel-DEVS Simulations with Partitioning Techniques Vision Based Dynamic Offside Line Marker for Soccer Games Presentation Formats, Performance Outcomes, and Implications for Performance Evaluations The Effectiveness of Graphical Presentation for Information Extraction: A Cumulative Experimental Approach* Towards a Neural Statistician Generative Partition Networks for Multi-Person Pose Estimation Graph-Structured Representations for Visual Question Answering Structural Neural Encoders for AMR-to-text Generation Gated Graph Sequence Neural Networks Recurrent Relational Networks Learning Convolutional Neural Networks for Graphs Neural Message Passing for Quantum Chemistry SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Structure Learning for Headline Generation Graph Similarity Metric Using Graph Convolutional Network: Application to Malware Similarity Match Graph convolutional networks for exploring authorship hypotheses How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty Heuristics for Image Generation from Scene Graphs Semi-Supervised Classification with Graph Convolutional Networks Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Graph Capsule Convolutional Neural Networks Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Graph Attention Networks Hypernetwork Knowledge Graph Embeddings Logic-Based Networks: Concept Graphs and Conceptual Structures Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules A mineração de dados e a qualidade de conhecimentos extraídos dos boletins de ocorrência das rodovias federais brasileiras Evaluation of Machine Learning Methods for Natural Language Processing Tasks SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning A Psychogenetic Algorithm for Behavioral Sequence Learning Integrating Neural and Symbolic Approaches: A Symbolic Learning Scheme for a Connectionist Associative Memory Machine discovery of protein motifs Efficient Multisplitting on Numerical Data Personalisation for user agents Towards Symbolic Reinforcement Learning with Common Sense Learning in an active hybrid vision system TransGate: Knowledge Graph Embedding with Shared Gate Structure Robust Graph Convolutional Networks Against Adversarial Attacks RGCN: Recurrent Graph Convolutional Networks for Target-Dependent Sentiment Analysis How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System Prediction of Drug–Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model RMol: a toolset for transforming SD/Molfile structure information into R objects Quantitative phosphoproteomics-based molecular network description for high-resolution kinase-substrate interactome analysis Design and analysis of a biomolecular positive-feedback oscillator Erratum to: On Different Aspects of Network Analysis in Systems Biology
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International Encyclopedia of Statistical Science AAAI SIMULTECH ICLR ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NAACL-HLT ICLR NeurIPS ICML ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAAI LaTeCH@NAACL-HLT CIKM ICLR ArXiv ArXiv ECCV ICLR ICANN ICCS COLING LREC AAAI PKDD AAMAS ICPR AAAI KDD KSEM (1) CDC
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