In the enterprise context , people need to exploit and mainly visualize different types of interactions between heterogeneous objects . Graph model seems to be the most appropriate way to represent those interactions . However , the graphs extracted have in general a huge size which makes it difficult to analyze and visualize . Graph algorithms that involve complex conditions on subgraphs can be specified much easier , if the specification allows expressions in higher-order logic to be used . In this paper an extension of Abstract State Machines by such expressions is introduced and its usefulness is demonstrated by examples of computations on graphs , such as graph factoring and checking self-similarity . Graphs are a powerful and versatile tool useful in various subfields of science and engineering . In many applications , for example , in pattern recognition and computer vision , it is required to measure the similarity of objects . A hot point based index is built and efficiently maintained for each query so as to greatly speed-up query time and achieve high system throughput . The GraphS system is developed at Alibaba to actively monitor various online fraudulent activities based on cycle detection . For a dynamic graph with hundreds of millions of edges and vertices , the system is capable to cope with a peak rate of tens of thousands of edge updates per second and find all the cycles with predefined constraints with a 99.9 % latency of 20 milliseconds . Our proposed matchmaker algorithm performs semantic matching of Web Services on the basis of input and output descriptions of semantic Web Services matching . This technique takes advantages from a graph structure and flow networks . Our novel approach is assigning matchmaking scores to semantics of the inputs and outputs parameters and their types . This paper introduces research leading to a computer-aided design tool in which engineering designers can test various concepts in an environment equipped to automatically model the dynamics and then optimize the chosen components to best meet the specifications of the design problem . The input to the system is a graph of components where the components either include defined parameter values or are left as variables for the subsequent optimization . In this paper , “ conceptual dynamics ” is introduced with the goal of revealing the qualitative information of the system states , energy flow patterns , possible operation modes , degrees of freedom , etc . Given the characteristics of graph structures , complexes , units , and systems created from the modules of the graph . The method of modeling the system on the graph of modules , which describe in the programming languages ( LP ) and calling them with operations ( link , assembling , building , etc . ) . The standard of configuration ( 2012 ) Assembly of heterogeneous software elements in AS of different fields of knowledge is made . We show our hybrid algorithm for list ranking is faster by 50 % compared to the best known implementation [ Z. Wei , J. JaJa ; IPDPS 2010 ] . Similarly , our hybrid algorithm for graph connected components is faster by 25 % compared to the best known GPU implementation [ 26 ] . From the trajectory file of MD simulations and a structure file , users can generate several images and statistics of ion conduction processes . A complex network named ion-binding state graph is generated in a standard graph format ( graph modeling language ; GML ) , which can be visualized by standard network analyzers such as Cytoscape . As a tutorial , a trajectory of a 50 ns MD simulation of the Kv1.2 channel is also distributed with the toolkit . Inspired by their work , Romera propose an end - to - end recurrent network with convolutional LSTMs that sequentially outputs binary segmentation maps for each instance . Ren improve upon by adding a box network to confine segmentations within a local window and skip connections instead of graphical models to restore the resolution at the output . Their final framework consists of four major components : an external memory and networks for box proposal , segmentation and scoring . Our network design largely builds off of their work , exploring how to capture information across scales and adapting their method for combining features across different resolutions . A critical feature of the method proposed by Tompson et al . is the joint use of a ConvNet and a graphical model . Their graphical model learns typical spatial relationships between joints . Due to the general nature of the problem considered , our work touches on many different topics which we now attempt to summarize . paragraph : Topic models and graphical models The form of the graphical model in Figure [ reference ] on the left is equivalent to that of a standard topic model . The input to the MTGAE model is a graph of nodes . Graph is represented by its adjacency matrix paired with a unique ordering of vertices . For a partially observed graph , , where denotes a known positive edge , denotes a known negative edge , and unk denotes an unknown status ( missing or unobserved ) edge . In addition to the weighted sum pooing , also proposes a “ spatial regulariser ” that forces features of the highest scoring proposal and its spatially adjacent proposals to be the same . Unlike this , we show that finding proposal cluster centers using graph and treating proposal clusters as bags are more effective . The contemporary work uses a graph model to generate seed proposals . In Example ( 2 ) , only the graph model correctly generates the phrase you tell them , while none of the models use people as the subject of the predicate can . In Example ( 3 ) , both the graph and the sequential models deal well with the control structure caused by the recommend predicate . The sequential model , however , overgenerates a wh - clause . There have been several attempts in the literature to extend neural networks to deal with arbitrarily structured graphs . Early work used recursive neural networks to process data represented in graph domains as directed acyclic graphs [ reference ][ reference ] . Graph Neural Networks ( GNNs ) were introduced in [ reference ] and [ reference ] as a generalization of recursive neural networks that can directly deal with a more general class of graphs , e.g. cyclic , directed and undirected graphs . GNNs consist of an iterative process , which propagates the node states until equilibrium ; followed by a neural network , which produces an output for each node [ reference ] ) using updated 3D locations , and hence the image features from correct locations can be effectively integrated with 3D shapes . Given the graph representation , the next challenge is how to update the vertex location effectively towards ground truth . In practice , we observe that network trained to directly predict mesh with a large number of vertices is likely to make mistake in the beginning and hard to fix later . The children of a logical operator node are the columns on which this operator works . subsection : Graph Representation We use the following method to transform the SQL query to a graph : SELECT Clause . section : Introduction Graphs are one of the most fundamental structures that have been widely used for representing many types of data . Learning on graphs such as graph semi - supervised learning , graph classification or graph evolution have found wide applications in domains such as bioinformatics , chemoinformatics , social networks , natural language processing and computer vision . A GCN prototype system is constructed by integrating the GNS and the ATM/MPLS network . Demonstration results show that the architecture of the GCN proposed in this paper and the key technologies are feasible . The feature vector output of our LSTM model combined with other demographic and computed behavioral features of customers gave better prediction results . We have also iv proposed and developed a model to find out whether connection between the customers can assist in the churn prediction using Graph convolutional networks ( GCN ) ; which incorporate customer network connections defined over three dimensions This will have a significant impact on the underlying networks , which could become the system bottleneck . In this paper , we propose Genome Centric Networking ( GCN ) , a novel network function virtualization framework for cloud-based genomic data management , designed with the aim of limiting the exchanged traffic by using distributed caching . The key element of GCN is a novel signaling protocol , which allows both discovering network resources and managing caches . We also prove that DA similarity has a positive correlation with the final performance , through both theoretical analysis and empirical simulations . Second , we propose a simplified GCN architecture which employs a single GCN layer to aggregate information from the neighbors filtered by DA similarity and then generates the node representations . Moreover , the aggregation step is a parameter-free operation , such that it can be done in a pre-processing manner to further reduce red the training and inference costs . The other baseline methods are taken from Yang et al . . In this comparison , our LoNGAE model achieves competitive performance when compared against the GCN model on the Cora dataset , but outperforms GCN and all other baseline methods on the Citeseer and Pubmed datasets . width=0.5 In this section , we provide theoretical motivation for a specific graph - based neural network model that we will use in the rest of this paper . We consider a multi - layer Graph Convolutional Network ( GCN ) with the following layer - wise propagation rule : Here , is the adjacency matrix of the undirected graph with added self - connections . is the identity matrix , and is a layer - specific trainable weight matrix . 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 . Depending on the regularity of the graph structure in place , GPUs may not be able to offer major performance benefits compared to CPUs in these sparse scenarios . It should also be noted that the size of the " receptive field " of our model is upper - bounded by the depth of the network ( similarly as for GCN and similar models ) . Techniques such as skip connections [ reference ] could be readily applied for appropriately extending the depth , however . 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 ) . The other transductive approaches are either completely inappropriate in an inductive setting or assume that nodes are incrementally added to a single graph , making them unusable for the setup where test graphs are completely unseen during training ( such as the PPI dataset ) . In order to fairly assess the benefits of the attention mechanism , we further evaluate a GCN model that computes 64 hidden features , attempting both the ReLU and ELU activation , and reporting ( as GCN - 64 * ) the better result after 100 runs ( which was the ReLU in all three cases ) . For the inductive task , we report the micro - averaged F 1 score on the nodes of the two unseen test graphs , averaged after 10 runs , and reuse the metrics already reported in [ reference ] for [ reference ] 59.5 % 60.1 % 70.7 % SemiEmb [ reference ] 59.0 % 59.6 % 71.7 % LP [ reference ] 68.0 % 45.3 % 63.0 % DeepWalk [ reference ] 67.2 % 43.2 % 65.3 % ICA [ reference ] 75.1 % 69.1 % 73.9 % Planetoid [ reference ] 75.7 % 64.7 % 77.2 % Chebyshev [ reference ] 69.8 % 74.4 % GCN [ reference ] 81.5 % 70.3 % 79.0 % MoNet [ reference ] 81.7 ± 0.5 % - 78.8 ± 0.3 % GCN - 64 * 81.4 ± 0.5 % 70.9 ± 0.5 % 79.0 ± 0.3 % GAT ( ours ) 83.0 ± 0.7 % 72.5 ± 0.7 % 79.0 ± the best result we were able to achieve with GraphSAGE by just modifying its architecture ( this was with a three - layer GraphSAGE - LSTM with [ reference ][ reference ][ reference ] features computed in each layer and 128 features used for aggregating neighborhoods ) . Finally , we report the 10 - run result of our constant attention GAT model ( as Const - GAT ) , to fairly evaluate the benefits of the attention mechanism against a GCN - like aggregation scheme ( with the same architecture ) . Our results successfully demonstrate state - of - the - art performance being achieved or matched across all four datasets - in concordance with our expectations , as per the discussion in Section 2.2 . Link prediction results on all four datasets are shown in Tables [ reference ] and [ reference ] . Overall , TuckER outperforms previous state - of - the - art models on all metrics across all datasets ( apart from hits@10 on WN18 where a non - linear model , R - GCN , does better ) , which shows that this relatively simple yet fully flexible linear model leads to very good performance . Results achieved by TuckER are not only better than those of other linear models , such as DistMult , ComplEx and SimplE , but also better than the results of many more complex deep neural network and reinforcement learning architectures , e.g. R - GCN , MINERVA , ConvE and HypER , demonstrating the expressive power of linear models . We propose a neural multi-document summarization ( MDS ) system that incorporates sentence relation graphs . We employ a Graph Convolutional Network ( GCN ) on the relation graphs , with sentence embeddings obtained from Recurrent Neural Networks as input node features . Through multiple layer-wise propagation , the GCN generates high-level hidden sentence features for salience estimation . Specifically , we construct an urban knowledge graph ( KG ) that is organized by regions based on the multiple types of urban knowledge . The graph convolutional network ( GCN ) is introduced to extract features from the urban KG . These features are processed as the input of a spatio-temporal convolutional neural network . Experimental evaluation on three standard 3D shape benchmarks demonstrate the competitive performance of our approach in comparison with existing techniques . We also introduce a deep similarity network fusion framework for 3D shape classification using a graph convolutional neural network , which is an efficient and scalable deep learning model for graph-structured data . The proposed approach coalesces the geometrical discriminative power of geodesic moments and similarity network fusion in an effort to design a simple , yet discriminative shape descriptor . Representation learning for molecules is important for molecular properties prediction , material design , drug screening , etc . In this work a graph convolutional network architecture for learning representations for molecules is presented . An operation for convolving k-neighbourhood of a specific node in graph is defined , which is corresponding to kernel size of k in convolutional neural networks . This paper explores the recently proposed Graph Convolutional Network architecture proposed in ( Kipf & Welling , 2016 ) The key points of their work is summarized and their results are reproduced . Weakly-supervised semantic image segmentation suffers from lacking accurate pixel-level annotations . In this paper , we propose a novel graph convolutional network-based method , called GraphNet , to learn pixel-wise labels from weak annotations . Firstly , we construct a graph on the superpixels of a training image by combining the low-level spatial relation and high-level semantic content . However , designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task . Here we propose Graph Convolutional Policy Network ( GCPN ) , a general graph convolutional network based model for goal-directed graph generation through reinforcement learning . The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient , and acts in an environment that incorporates domain-specific rules . Abstract This paper proposes a new graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quantum walks , which we refer to as the quantum-based subgraph convolutional neural network ( QS-CNNs ) . This new architecture captures both the global topological structure and the local connectivity structure within a graph . Hamilton et al . succinctly articulate the diverse set of previously proposed approaches for graph representation learning , or graph embedding , as belonging within a unified encoder - decoder framework . In this section , we summarize three classes of encoder - decoder models most related to our work : matrix factorization ( MF ) , autoencoders , and graph convolutional networks ( GCNs ) . MF has its roots in dimensionality reduction and gained popularity with extensive applications in collaborative filtering ( CF ) and recommender systems . |F F + |E|F ) , where F is the number of input features , and |V | and |E| are the numbers of nodes and edges in the graph , respectively . This complexity is on par with the baseline methods such as Graph Convolutional Networks ( GCNs ) [ reference ] . Applying multi - head attention multiplies the storage and parameter requirements by a factor of K , while the individual heads ' computations are fully independent and can be parallelized . The serial of G - ResNet block produces a new 128 - dim 3D feature . In addition to the feature output , there is a branch which applies an extra graph convolutional layer to the last layer features and outputs the 3D coordinates of the vertex . subsection : Graph unpooling layer In this paper , a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network ( MPNN ) . The proposed model is developed to approximate multiple nonlinear model with reduced computational requirement . In this paper , an approach is proposed for time series prediction based on Modified Probability Neural Network ( MPNN ) . Bayesian-statistics and decision-making theories and non-parameters density function estimation using Parzen-window function are applied to MPNN . The efficiency of the approach was demonstrated by a case study , an application for prediction on moonlet power system data , through comparison with other methods the linear ARMA model and the nonlinear widely used BP neural network . It measures the received signal strength ( RSS ) between an object and stations , and then transforms the RSS into distances . A MPNN engine determines coordinate of the object with the input distances . The experiments are conducted in a realistic ZigBee sensor network . To that end , we describe a general framework for supervised learning on graphs called Message Passing Neural Networks ( MPNNs ) that simply abstracts the commonalities between several of the most promising existing neural models for graph structured data , in order to make it easier to understand the relationships between them and come up with novel variations . Given how many researchers have published models that fit into the MPNN framework , we believe that the community should push this general approach as far as possible on practically important graph problems and only suggest new variations that are well motivated by applications , such as the application we consider here : predicting the quantum mechanical properties of small organic molecules ( see task schematic in figure [ reference ] ) . In general , the search for practically effective machine learning ( ML ) models in a given domain proceeds through a sequence of increasingly realistic and interesting benchmarks . In the rest of this paper when we talk about chemical accuracy we generally mean with respect to our available ground truth labels . In this paper , by exploring novel variations of models in the MPNN family , we are able to both achieve a new state of the art on the QM9 dataset and to predict the DFT calculation to within chemical accuracy on all but two targets . In particular , we provide the following key contributions : We develop an MPNN which achieves state of the art results on all 13 targets and predicts DFT to within chemical accuracy on 11 out of 13 targets . We develop several different MPNNs which predict DFT to within chemical accuracy on 5 out of 13 targets while operating on the topology of the molecule alone ( with no spatial information as input ) . section : Message Passing Neural Networks There are at least eight notable examples of models from the literature that we can describe using our Message Passing Neural Networks ( MPNN ) framework . For simplicity we describe MPNNs which operate on undirected graphs with node features and edge features . Recent work has adapted the GG - NN architecture to larger graphs by passing messages on only subsets of the graph at each time step . In this work we also present a MPNN modification that can improve the computational costs . section : Related Work For a more detailed description of these properties , see the supplementary material . section : MPNN Variants We began our exploration of MPNNs around the GG - NN model which we believe to be a strong baseline . Treating undirected graphs as directed means that the size of the message channel is instead of . The input to our MPNN model is a set of feature vectors for the nodes of the graph , , and an adjacency matrix with vector valued entries to indicate different bonds in the molecule as well as pairwise spatial distance between two atoms . We experimented as well with the message function used in the GG - NN family , which assumes discrete edge labels , in which case the matrix has entries in a discrete alphabet of size . In some cases the improvement was up to 40 % . Our best MPNN variant used the edge network message function , set2set output , and operated on graphs with explicit hydrogens . We were able to further improve performance on the test set by ensembling the predictions of the five models with lowest validation error . The performance of CGNN is studied throughout three experiments . Firstly , CGNN is applied to cause-effect inference , where the task is to identify the best causal hypothesis out of “ X → Y ” and “ Y → X ” . Secondly , CGNN is applied to the problem of identifying v-structures and conditional independences . Using grey system ( GS ) theory , data mining ( DM ) and radial basis function ( RBF ) neural network method , a new model , the combined grey neural network ( CGNN ) model is setup , which aims at solving users ’ data transmission in E-commerce . The results show that , in short-term prediction of data transmission , GM is an effective way and RBF has perfect ability to study and map . In this paper , we investigate the global exponential stability for a class of Cohen-Grossberg neural networks ( CGNN ) with time-varying delays and continuously distributed delays . CGNN herein is a general neural networks model which includes some well-known neural networks as its special cases . Firstly , applying the homeomorphism theory , we establish the new sufficient condition of existence and uniqueness of the equilibrium point to CGNN . In this paper , a comprehensive application of M-Matrix technique , Lyapunov functional method , Yang inequality and variational methods demonstrates the unique existence of globally asymptotically stable trivial equilibrium solution for the fuzzy dynamical system with nonlinear p-Laplace ( p > 1 ) diffusion . Especially when p=2 , it becomes the reaction-diffusion fuzzy BAM Cohen-Grossberg neural network ( BAM CGNN ) . Or if diffusion phenomena are ignored , it degrades into the fuzzy ordinary differential BAM CGNN . With combination of grey neural network ( CGNN ) and grey relational clustering , the models are constructed , which are used to solve the prediction and comparison of surrounding rocks stability controlling parameters in deep entry of coal mine . The results show that grey relational clustering is an effective way and CGNN has perfect ability to be studied in a short-term prediction . By employing Lyapunov method and Lasalle-type theorem , the attractor of stochastic Cohen-Grossberg neural networks ( CGNN ) with delays is initially investigated . Novel results and sufficient criteria on the attractor of stochastic CGNN are obtained . The Connection Machine is a fine-grained parallel computer having up to 64 K processors . It supports both local communication among the processors , which are situated in a two-dimensional mesh , and high-bandwidth communication among processors at arbitrary locations , using a message- passing network . We present solutions to a set of Image Understanding problems for the Connection Machine . Training is unsupervised , we train the network on arbitrary ( unlabeled ) instances of the problems . Moreover , we experimentally show that it suffices to train on relatively small instances ; the resulting message passing network will perform well on much larger instances ( at least 10-times larger ) . Since object arrangement and scene layout are intrinsically coupled , we show that treating the problem jointly significantly helps to produce globally-consistent representations of a scene . Object CAD models are aligned to the scene by establishing dense correspondences between geometry , and we introduce a hierarchical layout prediction approach to estimate layout planes from corners and edges of the this http URL this end , we propose a message-passing graph neural network to model the inter-relationships between objects and layout , guiding generation of a globally object alignment in a scene . By considering the global scene layout , we achieve significantly improved CAD alignments compared to state-of-the-art methods , improving from 41.83 % to 58.41 % alignment accuracy on SUNCG and from 50.05 % to 61.24 % on ScanNet , respectively . A very different approach is proposed in where a set of local nodes are converted into a sequence in order to create receptive fields which are then fed into a 1D convolutional neural network . Another popular name for GCNNs is message passing neural networks ( MPNNs ) . Though the authors in suggests that GCNNs are a special case of MPNNs , we believe that both are equivalent models in a certain sense ; it is simply a matter of how the graph convolution operation is defined . At this point , the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach . In this paper , we reformulate existing models into a single common framework we call Message Passing Neural Networks ( MPNNs ) and explore additional novel variations within this framework . Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark ; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels .
In the enterprise context , people need to exploit and mainly visualize different types of interactions between heterogeneous objects . Graph model seems to be the most appropriate way to represent those interactions . However , the graphs extracted have in general a huge size which makes it difficult to analyze and visualize .
Graph algorithms that involve complex conditions on subgraphs can be specified much easier , if the specification allows expressions in higher-order logic to be used . In this paper an extension of Abstract State Machines by such expressions is introduced and its usefulness is demonstrated by examples of computations on graphs , such as graph factoring and checking self-similarity .
Graphs are a powerful and versatile tool useful in various subfields of science and engineering . In many applications , for example , in pattern recognition and computer vision , it is required to measure the similarity of objects .
A hot point based index is built and efficiently maintained for each query so as to greatly speed-up query time and achieve high system throughput . The GraphS system is developed at Alibaba to actively monitor various online fraudulent activities based on cycle detection . For a dynamic graph with hundreds of millions of edges and vertices , the system is capable to cope with a peak rate of tens of thousands of edge updates per second and find all the cycles with predefined constraints with a 99.9 % latency of 20 milliseconds .
Our proposed matchmaker algorithm performs semantic matching of Web Services on the basis of input and output descriptions of semantic Web Services matching . This technique takes advantages from a graph structure and flow networks . Our novel approach is assigning matchmaking scores to semantics of the inputs and outputs parameters and their types .
This paper introduces research leading to a computer-aided design tool in which engineering designers can test various concepts in an environment equipped to automatically model the dynamics and then optimize the chosen components to best meet the specifications of the design problem . The input to the system is a graph of components where the components either include defined parameter values or are left as variables for the subsequent optimization . In this paper , “ conceptual dynamics ” is introduced with the goal of revealing the qualitative information of the system states , energy flow patterns , possible operation modes , degrees of freedom , etc .
Given the characteristics of graph structures , complexes , units , and systems created from the modules of the graph . The method of modeling the system on the graph of modules , which describe in the programming languages ( LP ) and calling them with operations ( link , assembling , building , etc . ) . The standard of configuration ( 2012 ) Assembly of heterogeneous software elements in AS of different fields of knowledge is made .
We show our hybrid algorithm for list ranking is faster by 50 % compared to the best known implementation [ Z. Wei , J. JaJa ; IPDPS 2010 ] . Similarly , our hybrid algorithm for graph connected components is faster by 25 % compared to the best known GPU implementation [ 26 ] .
From the trajectory file of MD simulations and a structure file , users can generate several images and statistics of ion conduction processes . A complex network named ion-binding state graph is generated in a standard graph format ( graph modeling language ; GML ) , which can be visualized by standard network analyzers such as Cytoscape . As a tutorial , a trajectory of a 50 ns MD simulation of the Kv1.2 channel is also distributed with the toolkit .
Inspired by their work , Romera propose an end - to - end recurrent network with convolutional LSTMs that sequentially outputs binary segmentation maps for each instance . Ren improve upon by adding a box network to confine segmentations within a local window and skip connections instead of graphical models to restore the resolution at the output . Their final framework consists of four major components : an external memory and networks for box proposal , segmentation and scoring .
Our network design largely builds off of their work , exploring how to capture information across scales and adapting their method for combining features across different resolutions . A critical feature of the method proposed by Tompson et al . is the joint use of a ConvNet and a graphical model . Their graphical model learns typical spatial relationships between joints .
Due to the general nature of the problem considered , our work touches on many different topics which we now attempt to summarize . paragraph : Topic models and graphical models The form of the graphical model in Figure [ reference ] on the left is equivalent to that of a standard topic model .
The input to the MTGAE model is a graph of nodes . Graph is represented by its adjacency matrix paired with a unique ordering of vertices . For a partially observed graph , , where denotes a known positive edge , denotes a known negative edge , and unk denotes an unknown status ( missing or unobserved ) edge .
In addition to the weighted sum pooing , also proposes a “ spatial regulariser ” that forces features of the highest scoring proposal and its spatially adjacent proposals to be the same . Unlike this , we show that finding proposal cluster centers using graph and treating proposal clusters as bags are more effective . The contemporary work uses a graph model to generate seed proposals .
In Example ( 2 ) , only the graph model correctly generates the phrase you tell them , while none of the models use people as the subject of the predicate can . In Example ( 3 ) , both the graph and the sequential models deal well with the control structure caused by the recommend predicate . The sequential model , however , overgenerates a wh - clause .
There have been several attempts in the literature to extend neural networks to deal with arbitrarily structured graphs . Early work used recursive neural networks to process data represented in graph domains as directed acyclic graphs [ reference ][ reference ] . Graph Neural Networks ( GNNs ) were introduced in [ reference ] and [ reference ] as a generalization of recursive neural networks that can directly deal with a more general class of graphs , e.g. cyclic , directed and undirected graphs . GNNs consist of an iterative process , which propagates the node states until equilibrium ; followed by a neural network , which produces an output for each node
[ reference ] ) using updated 3D locations , and hence the image features from correct locations can be effectively integrated with 3D shapes . Given the graph representation , the next challenge is how to update the vertex location effectively towards ground truth . In practice , we observe that network trained to directly predict mesh with a large number of vertices is likely to make mistake in the beginning and hard to fix later .
The children of a logical operator node are the columns on which this operator works . subsection : Graph Representation We use the following method to transform the SQL query to a graph : SELECT Clause .
section : Introduction Graphs are one of the most fundamental structures that have been widely used for representing many types of data . Learning on graphs such as graph semi - supervised learning , graph classification or graph evolution have found wide applications in domains such as bioinformatics , chemoinformatics , social networks , natural language processing and computer vision .
A GCN prototype system is constructed by integrating the GNS and the ATM/MPLS network . Demonstration results show that the architecture of the GCN proposed in this paper and the key technologies are feasible .
The feature vector output of our LSTM model combined with other demographic and computed behavioral features of customers gave better prediction results . We have also iv proposed and developed a model to find out whether connection between the customers can assist in the churn prediction using Graph convolutional networks ( GCN ) ; which incorporate customer network connections defined over three dimensions
This will have a significant impact on the underlying networks , which could become the system bottleneck . In this paper , we propose Genome Centric Networking ( GCN ) , a novel network function virtualization framework for cloud-based genomic data management , designed with the aim of limiting the exchanged traffic by using distributed caching . The key element of GCN is a novel signaling protocol , which allows both discovering network resources and managing caches .
We also prove that DA similarity has a positive correlation with the final performance , through both theoretical analysis and empirical simulations . Second , we propose a simplified GCN architecture which employs a single GCN layer to aggregate information from the neighbors filtered by DA similarity and then generates the node representations . Moreover , the aggregation step is a parameter-free operation , such that it can be done in a pre-processing manner to further reduce red the training and inference costs .
The other baseline methods are taken from Yang et al . . In this comparison , our LoNGAE model achieves competitive performance when compared against the GCN model on the Cora dataset , but outperforms GCN and all other baseline methods on the Citeseer and Pubmed datasets . width=0.5
In this section , we provide theoretical motivation for a specific graph - based neural network model that we will use in the rest of this paper . We consider a multi - layer Graph Convolutional Network ( GCN ) with the following layer - wise propagation rule : Here , is the adjacency matrix of the undirected graph with added self - connections . is the identity matrix , and is a layer - specific trainable weight matrix .
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 .
Depending on the regularity of the graph structure in place , GPUs may not be able to offer major performance benefits compared to CPUs in these sparse scenarios . It should also be noted that the size of the " receptive field " of our model is upper - bounded by the depth of the network ( similarly as for GCN and similar models ) . Techniques such as skip connections [ reference ] could be readily applied for appropriately extending the depth , however .
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 ) . The other transductive approaches are either completely inappropriate in an inductive setting or assume that nodes are incrementally added to a single graph , making them unusable for the setup where test graphs are completely unseen during training ( such as the PPI dataset ) .
In order to fairly assess the benefits of the attention mechanism , we further evaluate a GCN model that computes 64 hidden features , attempting both the ReLU and ELU activation , and reporting ( as GCN - 64 * ) the better result after 100 runs ( which was the ReLU in all three cases ) . For the inductive task , we report the micro - averaged F 1 score on the nodes of the two unseen test graphs , averaged after 10 runs , and reuse the metrics already reported in [ reference ] for [ reference ] 59.5 % 60.1 % 70.7 % SemiEmb [ reference ] 59.0 % 59.6 % 71.7 % LP [ reference ] 68.0 % 45.3 % 63.0 % DeepWalk [ reference ] 67.2 % 43.2 % 65.3 % ICA [ reference ] 75.1 % 69.1 % 73.9 % Planetoid [ reference ] 75.7 % 64.7 % 77.2 % Chebyshev [ reference ] 69.8 % 74.4 % GCN [ reference ] 81.5 % 70.3 % 79.0 % MoNet [ reference ] 81.7 ± 0.5 % - 78.8 ± 0.3 % GCN - 64 * 81.4 ± 0.5 % 70.9 ± 0.5 % 79.0 ± 0.3 % GAT ( ours ) 83.0 ± 0.7 % 72.5 ± 0.7 % 79.0 ±
the best result we were able to achieve with GraphSAGE by just modifying its architecture ( this was with a three - layer GraphSAGE - LSTM with [ reference ][ reference ][ reference ] features computed in each layer and 128 features used for aggregating neighborhoods ) . Finally , we report the 10 - run result of our constant attention GAT model ( as Const - GAT ) , to fairly evaluate the benefits of the attention mechanism against a GCN - like aggregation scheme ( with the same architecture ) . Our results successfully demonstrate state - of - the - art performance being achieved or matched across all four datasets - in concordance with our expectations , as per the discussion in Section 2.2 .
Link prediction results on all four datasets are shown in Tables [ reference ] and [ reference ] . Overall , TuckER outperforms previous state - of - the - art models on all metrics across all datasets ( apart from hits@10 on WN18 where a non - linear model , R - GCN , does better ) , which shows that this relatively simple yet fully flexible linear model leads to very good performance . Results achieved by TuckER are not only better than those of other linear models , such as DistMult , ComplEx and SimplE , but also better than the results of many more complex deep neural network and reinforcement learning architectures , e.g. R - GCN , MINERVA , ConvE and HypER , demonstrating the expressive power of linear models .
We propose a neural multi-document summarization ( MDS ) system that incorporates sentence relation graphs . We employ a Graph Convolutional Network ( GCN ) on the relation graphs , with sentence embeddings obtained from Recurrent Neural Networks as input node features . Through multiple layer-wise propagation , the GCN generates high-level hidden sentence features for salience estimation .
Specifically , we construct an urban knowledge graph ( KG ) that is organized by regions based on the multiple types of urban knowledge . The graph convolutional network ( GCN ) is introduced to extract features from the urban KG . These features are processed as the input of a spatio-temporal convolutional neural network .
Experimental evaluation on three standard 3D shape benchmarks demonstrate the competitive performance of our approach in comparison with existing techniques . We also introduce a deep similarity network fusion framework for 3D shape classification using a graph convolutional neural network , which is an efficient and scalable deep learning model for graph-structured data . The proposed approach coalesces the geometrical discriminative power of geodesic moments and similarity network fusion in an effort to design a simple , yet discriminative shape descriptor .
Representation learning for molecules is important for molecular properties prediction , material design , drug screening , etc . In this work a graph convolutional network architecture for learning representations for molecules is presented . An operation for convolving k-neighbourhood of a specific node in graph is defined , which is corresponding to kernel size of k in convolutional neural networks .
This paper explores the recently proposed Graph Convolutional Network architecture proposed in ( Kipf & Welling , 2016 ) The key points of their work is summarized and their results are reproduced .
Weakly-supervised semantic image segmentation suffers from lacking accurate pixel-level annotations . In this paper , we propose a novel graph convolutional network-based method , called GraphNet , to learn pixel-wise labels from weak annotations . Firstly , we construct a graph on the superpixels of a training image by combining the low-level spatial relation and high-level semantic content .
However , designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task . Here we propose Graph Convolutional Policy Network ( GCPN ) , a general graph convolutional network based model for goal-directed graph generation through reinforcement learning . The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient , and acts in an environment that incorporates domain-specific rules .
Abstract This paper proposes a new graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quantum walks , which we refer to as the quantum-based subgraph convolutional neural network ( QS-CNNs ) . This new architecture captures both the global topological structure and the local connectivity structure within a graph .
Hamilton et al . succinctly articulate the diverse set of previously proposed approaches for graph representation learning , or graph embedding , as belonging within a unified encoder - decoder framework . In this section , we summarize three classes of encoder - decoder models most related to our work : matrix factorization ( MF ) , autoencoders , and graph convolutional networks ( GCNs ) . MF has its roots in dimensionality reduction and gained popularity with extensive applications in collaborative filtering ( CF ) and recommender systems .
|F F + |E|F ) , where F is the number of input features , and |V | and |E| are the numbers of nodes and edges in the graph , respectively . This complexity is on par with the baseline methods such as Graph Convolutional Networks ( GCNs ) [ reference ] . Applying multi - head attention multiplies the storage and parameter requirements by a factor of K , while the individual heads ' computations are fully independent and can be parallelized .
The serial of G - ResNet block produces a new 128 - dim 3D feature . In addition to the feature output , there is a branch which applies an extra graph convolutional layer to the last layer features and outputs the 3D coordinates of the vertex . subsection : Graph unpooling layer
In this paper , a model is proposed which combines multiple local linear models with a novel modified probabilistic neural network ( MPNN ) . The proposed model is developed to approximate multiple nonlinear model with reduced computational requirement .
In this paper , an approach is proposed for time series prediction based on Modified Probability Neural Network ( MPNN ) . Bayesian-statistics and decision-making theories and non-parameters density function estimation using Parzen-window function are applied to MPNN . The efficiency of the approach was demonstrated by a case study , an application for prediction on moonlet power system data , through comparison with other methods the linear ARMA model and the nonlinear widely used BP neural network .
It measures the received signal strength ( RSS ) between an object and stations , and then transforms the RSS into distances . A MPNN engine determines coordinate of the object with the input distances . The experiments are conducted in a realistic ZigBee sensor network .
To that end , we describe a general framework for supervised learning on graphs called Message Passing Neural Networks ( MPNNs ) that simply abstracts the commonalities between several of the most promising existing neural models for graph structured data , in order to make it easier to understand the relationships between them and come up with novel variations . Given how many researchers have published models that fit into the MPNN framework , we believe that the community should push this general approach as far as possible on practically important graph problems and only suggest new variations that are well motivated by applications , such as the application we consider here : predicting the quantum mechanical properties of small organic molecules ( see task schematic in figure [ reference ] ) . In general , the search for practically effective machine learning ( ML ) models in a given domain proceeds through a sequence of increasingly realistic and interesting benchmarks .
In the rest of this paper when we talk about chemical accuracy we generally mean with respect to our available ground truth labels . In this paper , by exploring novel variations of models in the MPNN family , we are able to both achieve a new state of the art on the QM9 dataset and to predict the DFT calculation to within chemical accuracy on all but two targets . In particular , we provide the following key contributions : We develop an MPNN which achieves state of the art results on all 13 targets and predicts DFT to within chemical accuracy on 11 out of 13 targets . We develop several different MPNNs which predict DFT to within chemical accuracy on 5 out of 13 targets while operating on the topology of the molecule alone ( with no spatial information as input ) .
section : Message Passing Neural Networks There are at least eight notable examples of models from the literature that we can describe using our Message Passing Neural Networks ( MPNN ) framework . For simplicity we describe MPNNs which operate on undirected graphs with node features and edge features .
Recent work has adapted the GG - NN architecture to larger graphs by passing messages on only subsets of the graph at each time step . In this work we also present a MPNN modification that can improve the computational costs . section : Related Work
For a more detailed description of these properties , see the supplementary material . section : MPNN Variants We began our exploration of MPNNs around the GG - NN model which we believe to be a strong baseline .
Treating undirected graphs as directed means that the size of the message channel is instead of . The input to our MPNN model is a set of feature vectors for the nodes of the graph , , and an adjacency matrix with vector valued entries to indicate different bonds in the molecule as well as pairwise spatial distance between two atoms . We experimented as well with the message function used in the GG - NN family , which assumes discrete edge labels , in which case the matrix has entries in a discrete alphabet of size .
In some cases the improvement was up to 40 % . Our best MPNN variant used the edge network message function , set2set output , and operated on graphs with explicit hydrogens . We were able to further improve performance on the test set by ensembling the predictions of the five models with lowest validation error .
The performance of CGNN is studied throughout three experiments . Firstly , CGNN is applied to cause-effect inference , where the task is to identify the best causal hypothesis out of “ X → Y ” and “ Y → X ” . Secondly , CGNN is applied to the problem of identifying v-structures and conditional independences .
Using grey system ( GS ) theory , data mining ( DM ) and radial basis function ( RBF ) neural network method , a new model , the combined grey neural network ( CGNN ) model is setup , which aims at solving users ’ data transmission in E-commerce . The results show that , in short-term prediction of data transmission , GM is an effective way and RBF has perfect ability to study and map .
In this paper , we investigate the global exponential stability for a class of Cohen-Grossberg neural networks ( CGNN ) with time-varying delays and continuously distributed delays . CGNN herein is a general neural networks model which includes some well-known neural networks as its special cases . Firstly , applying the homeomorphism theory , we establish the new sufficient condition of existence and uniqueness of the equilibrium point to CGNN .
In this paper , a comprehensive application of M-Matrix technique , Lyapunov functional method , Yang inequality and variational methods demonstrates the unique existence of globally asymptotically stable trivial equilibrium solution for the fuzzy dynamical system with nonlinear p-Laplace ( p > 1 ) diffusion . Especially when p=2 , it becomes the reaction-diffusion fuzzy BAM Cohen-Grossberg neural network ( BAM CGNN ) . Or if diffusion phenomena are ignored , it degrades into the fuzzy ordinary differential BAM CGNN .
With combination of grey neural network ( CGNN ) and grey relational clustering , the models are constructed , which are used to solve the prediction and comparison of surrounding rocks stability controlling parameters in deep entry of coal mine . The results show that grey relational clustering is an effective way and CGNN has perfect ability to be studied in a short-term prediction .
By employing Lyapunov method and Lasalle-type theorem , the attractor of stochastic Cohen-Grossberg neural networks ( CGNN ) with delays is initially investigated . Novel results and sufficient criteria on the attractor of stochastic CGNN are obtained .
The Connection Machine is a fine-grained parallel computer having up to 64 K processors . It supports both local communication among the processors , which are situated in a two-dimensional mesh , and high-bandwidth communication among processors at arbitrary locations , using a message- passing network . We present solutions to a set of Image Understanding problems for the Connection Machine .
Training is unsupervised , we train the network on arbitrary ( unlabeled ) instances of the problems . Moreover , we experimentally show that it suffices to train on relatively small instances ; the resulting message passing network will perform well on much larger instances ( at least 10-times larger ) .
Since object arrangement and scene layout are intrinsically coupled , we show that treating the problem jointly significantly helps to produce globally-consistent representations of a scene . Object CAD models are aligned to the scene by establishing dense correspondences between geometry , and we introduce a hierarchical layout prediction approach to estimate layout planes from corners and edges of the this http URL this end , we propose a message-passing graph neural network to model the inter-relationships between objects and layout , guiding generation of a globally object alignment in a scene . By considering the global scene layout , we achieve significantly improved CAD alignments compared to state-of-the-art methods , improving from 41.83 % to 58.41 % alignment accuracy on SUNCG and from 50.05 % to 61.24 % on ScanNet , respectively .
A very different approach is proposed in where a set of local nodes are converted into a sequence in order to create receptive fields which are then fed into a 1D convolutional neural network . Another popular name for GCNNs is message passing neural networks ( MPNNs ) . Though the authors in suggests that GCNNs are a special case of MPNNs , we believe that both are equivalent models in a certain sense ; it is simply a matter of how the graph convolution operation is defined .
At this point , the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach . In this paper , we reformulate existing models into a single common framework we call Message Passing Neural Networks ( MPNNs ) and explore additional novel variations within this framework . Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark ; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels .
Graph Aggregation : Application to Social Networks Systematic Refinement of Abstract State Machines with Higher-Order Logic Recent developments in graph matching Real-time Constrained Cycle Detection in Large Dynamic Graphs A Three Phase Semantic Web Matchmaker A Design Representation to Support the Automatic Dynamic Evaluation of Electromechanical Designs The Theory Graph Modeling and Programming Systems from Module Elements to the Application Areas Hybrid algorithms for list ranking and graph connected components IBiSA_Tools: A Computational Toolkit for Ion-Binding State Analysis in Molecular Dynamics Trajectories of Ion Channels Semantic Instance Segmentation with a Discriminative Loss Function Stacked Hourglass Networks for Human Pose Estimation Towards a Neural Statistician Multi-Task Graph Autoencoders PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Structural Neural Encoders for AMR-to-text Generation Graph Attention Networks Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Graph Capsule Convolutional Neural Networks Research on architecture and key technologies of the grid communication network Churn prediction using customers' implicit behavioral patterns and deep learning Genome centric networking: A network function virtualization solution for genomic applications Single-Layer Graph Convolutional Networks For Recommendation Learning to Make Predictions on Graphs with Autoencoders Semi-Supervised Classification with Graph Convolutional Networks Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Graph Attention Networks TuckER: Tensor Factorization for Knowledge Graph Completion Graph-based Neural Multi-Document Summarization Urban Congestion Areas Prediction By Combining Knowledge Graph And Deep Spatio-Temporal Convolutional Neural Network Geometric Deep Learned Descriptors for 3D Shape Recognition Graph Convolutional Networks for Molecules Probabilistic and Regularized Graph Convolutional Networks GraphNet: Learning Image Pseudo Annotations for Weakly-Supervised Semantic Segmentation Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation Quantum-based subgraph convolutional neural networks Learning to Make Predictions on Graphs with Autoencoders Graph Attention Networks Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Vector quantized radial basis function neural network with embedded multiple local linear models for financial prediction Prediction on moonlet power system data based on modified probability neural network A modified probability neural network indoor positioning technique Neural Message Passing for Quantum Chemistry Learning Functional Causal Models with Generative Neural Networks Application of DM and Combined Grey Neural Network in E-Commerce Data Transmission Global Exponential Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays and Continuously Distributed Delays On the role of diffusion factors in stability analysis for p-Laplace dynamical equations involved to BAM Cohen-Grossberg neural network Application of grey relational clustering and CGNN in analyzing stability control of surrounding rocks in deep entry of coal mine Attractor and boundedness for stochastic Cohen-Grossberg neural networks with delays Parallel Algorithms for Computer Vision on the Connection Machine RUN-CSP: Unsupervised Learning of Message Passing Networks for Binary Constraint Satisfaction Problems SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans Graph Capsule Convolutional Neural Networks Neural Message Passing for Quantum Chemistry
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HDSDA ABZ ICPR HiPC ArXiv ECCV ICLR ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence NAACL-HLT ICLR ECCV ArXiv ArXiv NetSoft 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) ICLR ArXiv ICLR EMNLP/IJCNLP ACM Multimedia 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) ICLR ECCV ICML ISNN (4) ArXiv ICML
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