We tested our methodology on a wide variety of domains ranging from prostate gene expression , ovarian proteomic spectra , brain magnetic resonance imaging , and breast histopathology . Across these various high dimensional biomedical datasets with 100 + observations each and all parameters considered , the median classification accuracy across all experiments showed AdDReSS ( 88.7 % ) to outperform SSAGE , a SSDR method using random sampling ( 85.5 % ) , and Graph Embedding ( 81.5 % ) . Furthermore , we found that embeddings generated via AdDReSS achieved a mean 35.95 % improvement in Raghavan efficiency , a measure of learning rate , over SSAGE . To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage , machine leaning ( ML ) has been introduced recently to address the wireless link scheduling problems . In this paper , we propose a novel graph embedding based method for link scheduling in D2D networks . We first construct a fully-connected directed graph for the D2D network , where each D2D pair is a node while interference links among D2D pairs are the edges . Recently , many methods have been proposed for regularities discovery , but this task is challenging due to the quantity , sparsity and free-style of prescriptions . In this paper , we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions . We model this task as a relationprediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relations . Moreover , location-based recommendation requires spatiotemporal context awareness and dynamic tracking of the user 's latest preferences in a real-time manner . To address these challenges , we stand on recent advances in embedding learning techniques and propose a generic graph-based embedding model , called GE , in this paper . GE jointly captures the sequential effect , geographical influence , temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs ( POI-POI , POI-Region , POI-Time and POI-Word)into a shared low dimensional space . In this paper , we focus on the new problem of joint event-partner recommendation in EBSNs , which is extremely challenging due to the intrinsic cold-start property of events , the complex decision-making process for choosing event-partner pairs and the huge prediction space of event-partner combinations . We propose a generic graph-based embedding model ( GEM ) to collectively embed all the observed relations among users , events , locations , time and text content in a shared low-dimension space , which is able to leverage the correlation between events and their associated content and contextual information to address the cold-start issue effectively . To accelerate the convergence of GEM and improve its modeling accuracy , an adaptive noise sampler is developed to generate adversarial negative samples in the model optimization . If Babai 's result holds under scrutiny , it is likely to become one of the most celebrated results in theoretical computer science of the past several decades . Graph isomorphism has long tantalized researchers as it was not known to be solvable in polynomial time , yet there are good reasons … The class of connected graphs called ClassH contains each graph with number of edges less or equal to half number of edges of the complete graph with the same number of vertices . If a graph G∈ClassH then the basic algorithms are applied directly to G. Otherwise , if G∉ClassH we find G ( it has the same automorphim group ) , decompose G into connected components that belong to ClassH and apply the basic graph automorphsm algorithm to each component and the graph isomorphism algorithm between each of them . The polynomial complexities are based on a conjecture that the maximum value of the selection level is a constant 6 for any such graph . Just like BinDiff , a comparison method based on structure , can not be effective for judging the semantic differences . Though the other two tools(BindHunt and iBinHunt ) can recognize the differences we focus on , they can not effectively verify the functional inlining and spend a pretty long time to finish the process because the use of graph-based isomorphism algorithm . In the paper , we first propose SemDiff , which uses the existing tool(angr ) to generate the intermediate language(VEX ) . Recent work has addressed this problem for Simulink models [ 5 , 6 ] but these algorithms are unsuitable for application directly to Max/MSP . Control flow in Max/MSP is dependent on the spatial relationships of the objects used in a patch , thus graph isomorphic approaches such as that of Pham et al. [ 5 ] can not be applied . The readout phase computes a feature vector for the whole graph using some readout function according to The message functions , vertex update functions , and readout function are all learned differentiable functions . operates on the set of node states and must be invariant to permutations of the node states in order for the MPNN to be invariant to graph isomorphism . In what follows , we define previous models in the literature by specifying the message function , vertex update function , and readout function used . We present DeepWalk , a novel approach for learning latent representations of vertices in a network . These latent representations encode social relations in a continuous vector space , which is easily exploited by statistical models . DeepWalk 's representations can provide F1 scores up to 10 % higher than competing methods when labeled data is sparse . In some experiments , DeepWalk 's representations are able to outperform all baseline methods while using 60 % less training data . DeepWalk is also scalable . In this paper , traditional DeepWalk method and broad learning system ( BLS ) are used to classify network nodes in graph embedding , and results of classification are compared . When categorizing , DeepWalk adopts one vs rest ( OvR ) logistic regression method , and BLS is applied after the production of vector representations . To address this issue , we propose to learn the latent social listening representations by the DeepWalk method , and then integrate the learned representations into Factorization Machines to construct better recommendation models . With the DeepWalk method , user social relationships can be transformed from the sparse and independent and identically distributed ( i.i.d . ) form into a dense and noni.i.d . form . Node2vec improves the random walk procedures , thus improving the performance of node2vec algorithm on various tasks . Therefore , we propose an improved DeepWalk algorithm based on preference random walk ( PDW ) , which modifies the single undirected edge into two one-way directed edges in the network , and then gives each one-way directed edge a walk probability based on local random walk algorithm . In the procedures of acquiring walk sequences , the walk probability of the paths that have been walked will be attenuated according to the attenuation coefficient . However , the research work on discovering communities in multi-relational network ( MRN ) is still in its early stages . In this article , we have proposed a novel approach to extract the communities in a multi-relational network using DeepWalk network embedding technique and Consensus clustering . Empirical study is conducted on the real-world publicly available Twitter datasets . Network embedding based on the random walk and skip-gram model such as the DeepWalk and Node2Vec algorithms have received wide attention . We identify that these algorithms essentially estimate the node similarities by random walk simulation , which is unreliable , inefficient , and inflexible . However , vertices in lots of networks may contain community information or text contents , which could be good for relevant evaluation tasks , such as vertex classification , link prediction and so on . Since it has been proved that DeepWalk is actually equivalent to matrix factorization , we propose community and text-enhanced DeepWalk ( CTDW ) based on the inductive matrix completion algorithm , which incorporates community features and text features of vertices into NRL under the framework of matrix factorization . In experiments , we evaluate the proposed CTDW compared with other state-of-the-art methods on vertex classification . This model learns low dimensional vectors to represent vertices appearing in a graph and , unlike existing work , integrates global structural information of the graph into the learning process . We also formally analyze the connections between our work and several previous research efforts , including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al. We conduct experiments on a language network , a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering , classification and visualization . Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks . We first introduce the definition of serendipitous collaborators from three components of serendipity : relevance , unexpectedness , and value , respectively . Then we improve the transition probability of randomwalk in DeepWalk , and propose a serendipity-biased DeepWalk , called Seren2vec . The walker jumps to the next neighbor node with the proportional probability of edge weight in the co-author network . DeepWalk & MFDW : For original random-walk based DeepWalk we set the window size to 5 . We also have MFDW aka NMF : S in Eqn : 3.2 the objective function for Matrix Factorized DeepWalk , as we build our model incrementally on top of it . Max-Margin DeepWalk ( MMDW ) : In this paper [ 6 ] , a max-margin loss is incorporated in the objective function of MFDW to learn discriminative representations of vertices . Recently , attention has shifted to models that learn graph embeddings with methods inspired by the skip - gram model mikolov2013distributed . DeepWalk perozzi2014deepwalk learns embeddings via the prediction of the local neighborhood of nodes , sampled from random walks on the graph . LINE tang2015line and node2vec grovernode2vec extend DeepWalk with more sophisticated random walk or breadth - first search schemes . Despite the recent progress , a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way . Here , we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network ( GIN ) , which was recently proposed as a state-of-the-art GNN for graph classification . One important observation in this paper is that the GIN is a realization of convolutional neural network ( CNN ) with two-tab filters in the graph space where the shift operation is realized using the adjacent matrix . Abstract . The Swiss Joint Information Platform for Natural Hazards ( GIN ) has been realized from 2008 to 2010 as part of the Swiss federal government ’s OWARNA project , which aimed at optimizing warning and alerting procedures against natural hazard . The first online-version of the platform went productive in 2011 with the primary goal of providing measured and forecast natural hazard data in form of processed cartographic , graphic and other multimedia products to professional users – before , during and after natural hazard events . User acceptance testing by means of surveys and direct user feedback sessions were key factors in this perennial redesign process . The GIN platform now features important novel technical and graphical elements : The starting page is based on a dashboard containing virtual dossiers ( Fig. 1 ) , with which users configure their desired information , data , and map bundles individually , or use predefined adaptable views on various existing data sets . In addition , there is a new overall spatial search function to query data parameters . Stakeholders not only use GIN ’s ample database and cartographic product portfolio to accomplish their early warning and crisis management tasks , but also benefit from seamless , secure and reliable IT-services , provided by the Swiss Federal Government . With the new GIN platform , Switzerland has a powerful , integrative , and comprehensive tool for monitoring and responding to natural hazard events . An ultrafast functional magnetic resonance imaging ( fMRI ) technique , called generalized inverse imaging ( GIN ) , is proposed , which combines inverse imaging with a phase constraint leading to a less underdetermined reconstruction and physiological noise correction . A single 3D echo planar imaging ( EPI ) prescan is sufficient to obtain the necessary coil sensitivity information and reference images that are used to reconstruct standard images , so that standard analysis methods are applicable . Gamma Interconnection Network ( GIN ) is a type of Multistage Interconnection Network ( MIN ) that consists of layers of switching elements connected together in a predefined topology providing the connectivity between the set of processors and the set of memory modules . GIN is constructed by 1x3 , 3x3 and 3x1 switching elements . By introducing co-occurrence relationship of commodities to explore the potential preferences , the weak generalization problem is also alleviated . To the best of our knowledge , the GIN method is the first to introduce graph learning for user intention mining in CTR prediction and propose end-to-end joint training of graph learning and CTR prediction tasks in sponsored search . At present , GIN has achieved excellent offline results on the real-world data of the e-commerce platform outperforming existing deep learning models , and has been running stable tests online and achieved significant CTR improvements . This paper introduces the design process and the function of the GIN data collection system of grouting procedure and engineering database , using the technique of object-oriented user interface and the object-oriented programming design language Visual C++ 2 . 0 . This system implements perfectly the collection of the necessary data of grouting procedure . With the leading of the GIN theory which is in fashion currently , it implements the treatment and printout of the relative dato . Specifically , a complementary AISEL dataset is generated , with labels actively acquired via a physics-based method to incorporate underlining physical knowledge at hand . An important component of our AISEL method is the bidirectional generative invertible network ( GIN ) , which can extract interpretable features from the training images and generate physically meaningful virtual images . Our AISEL method then efficiently samples virtual images not only further exploits the uncertain regions but also explores the entire image space . The proposed framework can be used to assess and characterize individual disaster situations so that the GIN functional and design requirements can be accurately identified early . Future implications to GIN architecture are also discussed . This paper introduces a user-friendly browser interface which integrates multilingual search and browsing functionalities within medical thesauri via the internet . The tool is being developed as part of the GIN Austria Patient Information System and is based on an adapted datamodel of the MeSH thesaurus . A prototype offers the possibility to build up queries and export lists of MeSH main headings collected during browsing the relevant MeSH trees . The Martini Working Group is defining a mechanism for SIP IP-PBX type devices to REGISTER and obtain SIP service for E.164-based Address of Records , using the GIN mechanism defined in [ draft-gin ] . Two other drafts , [ draft-olive ] and [ draft-glass ] , propose the same for non-E.164-based AoRs . However , existing network embedding methods mostly focus on unsigned networks and ignore the negative interactions between users . In this paper , we investigate the problem of learning representations for signed networks and present a novel deep network structure to incorporate both the balance and status theory in signed networks . With the proposed framework , we can simultaneously learn the node embedding encoding the status of a node and the edge embedding denoting the sign of an edge . Comparing to other methods , the DPC method guarantees generating more robust representation points . Random mapping of the low-dimensional features in the deep embedding network guarantees the robustness of the method . The experimental results on real polarimetric SAR data demonstrate the effectiveness of the proposed method . While a few deep neural networks have been adopted to capture the highly non-linearity , the deep structure makes them difficult to optimize in practice . In this paper , we propose a novel deep network embedding method , which exploits the fast learning speeds of extreme learning machine ( ELM ) . Particularly , we first design a deep ELM-based auto-encoder , based on which we then proposed an extended model to preserve both first-order and second-order proximities by a joint loss function . Results We proposed a deep learning based method , DeepHE , to predict human essential genes by integrating features derived from sequence data and protein-protein interaction ( PPI ) network . A deep learning based network embedding method was utilized to automatically learn features from PPI network . In addition , 89 sequence features were derived from DNA sequence and protein sequence for each gene . Abstract With the development of e-commerce , various methodologies have studied to improve recommendation performance . Recently , many deep learning based network embedding approaches are applied to the recommendation domain . However , these approaches still have several limitations , such as the problem of data sparseness and the changing in user preference over time , which can not be considered . Therefore , how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem . To solve this problem , in this paper we propose a Structural Deep Network Embedding method , namely SDNE . More specifically , we first propose a semi-supervised deep model , which has multiple layers of non-linear functions , thereby being able to capture the highly non-linear network structure . More specifically , we defined the molecular associations network and constructed a molecular associations dataset containing 105546 associations . Then , each node is represented by its attribute feature and network embedding learned by Structural Deep Network Embedding . Moreover , Random Forest is trained to predict any kind of associations . Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes . A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability . To keep the local network neighborhood , we further perform a Laplacian supervised space optimization on the embedding feature representations . section : Conclusion The main idea of the novel deep network architecture described in this paper involves increasing the feature map dimension gradually , in order to construct so - called PyramidNets along with the concept of ResNets . We also developed a novel residual unit , which includes a new building block for a residual unit with a zero - padded shortcut ; this design leads to significantly improved generalization ability . The former includes surface normal constraints and information propagation along edges ; the latter includes perceptual features extracted from images as a guidance . We carefully design our network structure and propose a very deep cascaded graph convolutional neural network with “ shortcut ” connections . Meshes are progressively refined by our network trained end - to - end with the chamfer loss and normal loss . Also related are conventional metric learning approaches that focus on learning a shallow ( linear ) Mahalanobis metric for a fixed feature representation . In contrast to prior work ’s fixed metric or fixed features and shallow learned metric , Relation Network can be seen as both learning a deep embedding and learning a deep non - linear metric ( similarity function ) . These are mutually tuned end - to - end to support each other in few short learning . The emotion Contempt of the CKP set is not shown as no subject with consent for publication and an annotated emotion is part of the dataset . Results of experiments on these datasets demonstrate the success of using a deep layered neural network structure . With a 10 - fold cross - validation a recognition accuracy of 99.6 % has been achieved . Our proposed xDeepFM achieves the best performance on all datasets , which demonstrates that combining explicit and implicit high - order feature interaction is necessary , and xDeepFM is effective in learning this class of combination . Another interesting observation is that , all the neural - based models do not require a very deep network structure for the best performance . Typical settings for the depth hyper - parameter are 2 and 3 , and the best depth setting for xDeepFM is 3 , which indicates that the interactions we learned are at most 4 - order . [ Neural Variational Document Model ] [ Semantic Word Vector ] section : Details of the Deep Neural Network Structures subsection : Neural Variational Document Model DeepWalk , LINE , and node2vec do not support external node / edge features . Our work is inspired by recent successful applications of autoencoder architectures for collaborative filtering that outperform popular matrix factorization methods , and is related to Structural Deep Network Embedding ( SDNE ) for link prediction . Similar to SDNE , our models rely on the autoencoder to learn non - linear node embeddings from local graph neighborhoods . We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness , i.e. , the random variation of their outcomes given identical algorithms and graphs . We apply five node embeddings algorithms --- HOPE , LINE , node2vec , SDNE , and GraphSAGE --- to synthetic and empirical graphs and assess their stability under randomness with respect to ( i ) the geometry of embedding spaces as well as ( ii ) their performance in downstream tasks . We find significant instabilities in the geometry of embedding spaces independent of the centrality of a node . The usefulness of NEMII is further validated by case studies . The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction , and SDNE outperforms other popular network embedding methods : DeepWalk , High-Order Proximity preserved Embedding ( HOPE ) and Laplacian Eigenmaps (LE).ConclusionWe propose a new method , named NEMII , for predicting miRNA-disease associations , which has great potential to benefit the field of miRNA-disease association prediction . In order to avoid the disadvantages of Quantuminspired shuffled frog leaping algorithm ( QSFLA ) , a fuzzy membership matrix with spatial information model is developed , which can remove isolated regions and further improve the detection accuracy . Segmentation , distribution and noise entropy ( SDNE ) model is also proposed to quantitatively evaluate the detection results . The detection results of the original sonar images demonstrate the effectiveness and adaptability of the proposed method . In comparison to the baselines , we evaluate our MTGAE model on the same data splits over 10 runs with random weight initialization and report mean AUC / AP scores for link prediction and accuracy scores for node classification . We also compare the representation capacity of our MTGAE model against the related autoencoder - based SDNE model on the network reconstruction task . We use the ranking metric precision@ to evaluate the model Results and Analysis Results of the reconstruction task for the Arxiv - GRQC and BlogCatalog network datasets are illustrated in Figure [ reference ] . In comparison to SDNE , we show that our MTGAE model achieves better precision@ performance for all values , up to for Arxiv - GRQC and for BlogCatalog , when trained on the complete datasets . We also systematically test the capacity of the MTGAE model to reconstruct the original networks when up to 80 percent of the edges are randomly removed , akin to the link prediction task .
We tested our methodology on a wide variety of domains ranging from prostate gene expression , ovarian proteomic spectra , brain magnetic resonance imaging , and breast histopathology . Across these various high dimensional biomedical datasets with 100 + observations each and all parameters considered , the median classification accuracy across all experiments showed AdDReSS ( 88.7 % ) to outperform SSAGE , a SSDR method using random sampling ( 85.5 % ) , and Graph Embedding ( 81.5 % ) . Furthermore , we found that embeddings generated via AdDReSS achieved a mean 35.95 % improvement in Raghavan efficiency , a measure of learning rate , over SSAGE .
To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage , machine leaning ( ML ) has been introduced recently to address the wireless link scheduling problems . In this paper , we propose a novel graph embedding based method for link scheduling in D2D networks . We first construct a fully-connected directed graph for the D2D network , where each D2D pair is a node while interference links among D2D pairs are the edges .
Recently , many methods have been proposed for regularities discovery , but this task is challenging due to the quantity , sparsity and free-style of prescriptions . In this paper , we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions . We model this task as a relationprediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relations .
Moreover , location-based recommendation requires spatiotemporal context awareness and dynamic tracking of the user 's latest preferences in a real-time manner . To address these challenges , we stand on recent advances in embedding learning techniques and propose a generic graph-based embedding model , called GE , in this paper . GE jointly captures the sequential effect , geographical influence , temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs ( POI-POI , POI-Region , POI-Time and POI-Word)into a shared low dimensional space .
In this paper , we focus on the new problem of joint event-partner recommendation in EBSNs , which is extremely challenging due to the intrinsic cold-start property of events , the complex decision-making process for choosing event-partner pairs and the huge prediction space of event-partner combinations . We propose a generic graph-based embedding model ( GEM ) to collectively embed all the observed relations among users , events , locations , time and text content in a shared low-dimension space , which is able to leverage the correlation between events and their associated content and contextual information to address the cold-start issue effectively . To accelerate the convergence of GEM and improve its modeling accuracy , an adaptive noise sampler is developed to generate adversarial negative samples in the model optimization .
If Babai 's result holds under scrutiny , it is likely to become one of the most celebrated results in theoretical computer science of the past several decades . Graph isomorphism has long tantalized researchers as it was not known to be solvable in polynomial time , yet there are good reasons …
The class of connected graphs called ClassH contains each graph with number of edges less or equal to half number of edges of the complete graph with the same number of vertices . If a graph G∈ClassH then the basic algorithms are applied directly to G. Otherwise , if G∉ClassH we find G ( it has the same automorphim group ) , decompose G into connected components that belong to ClassH and apply the basic graph automorphsm algorithm to each component and the graph isomorphism algorithm between each of them . The polynomial complexities are based on a conjecture that the maximum value of the selection level is a constant 6 for any such graph .
Just like BinDiff , a comparison method based on structure , can not be effective for judging the semantic differences . Though the other two tools(BindHunt and iBinHunt ) can recognize the differences we focus on , they can not effectively verify the functional inlining and spend a pretty long time to finish the process because the use of graph-based isomorphism algorithm . In the paper , we first propose SemDiff , which uses the existing tool(angr ) to generate the intermediate language(VEX ) .
Recent work has addressed this problem for Simulink models [ 5 , 6 ] but these algorithms are unsuitable for application directly to Max/MSP . Control flow in Max/MSP is dependent on the spatial relationships of the objects used in a patch , thus graph isomorphic approaches such as that of Pham et al. [ 5 ] can not be applied .
The readout phase computes a feature vector for the whole graph using some readout function according to The message functions , vertex update functions , and readout function are all learned differentiable functions . operates on the set of node states and must be invariant to permutations of the node states in order for the MPNN to be invariant to graph isomorphism . In what follows , we define previous models in the literature by specifying the message function , vertex update function , and readout function used .
We present DeepWalk , a novel approach for learning latent representations of vertices in a network . These latent representations encode social relations in a continuous vector space , which is easily exploited by statistical models .
DeepWalk 's representations can provide F1 scores up to 10 % higher than competing methods when labeled data is sparse . In some experiments , DeepWalk 's representations are able to outperform all baseline methods while using 60 % less training data . DeepWalk is also scalable .
In this paper , traditional DeepWalk method and broad learning system ( BLS ) are used to classify network nodes in graph embedding , and results of classification are compared . When categorizing , DeepWalk adopts one vs rest ( OvR ) logistic regression method , and BLS is applied after the production of vector representations .
To address this issue , we propose to learn the latent social listening representations by the DeepWalk method , and then integrate the learned representations into Factorization Machines to construct better recommendation models . With the DeepWalk method , user social relationships can be transformed from the sparse and independent and identically distributed ( i.i.d . ) form into a dense and noni.i.d . form .
Node2vec improves the random walk procedures , thus improving the performance of node2vec algorithm on various tasks . Therefore , we propose an improved DeepWalk algorithm based on preference random walk ( PDW ) , which modifies the single undirected edge into two one-way directed edges in the network , and then gives each one-way directed edge a walk probability based on local random walk algorithm . In the procedures of acquiring walk sequences , the walk probability of the paths that have been walked will be attenuated according to the attenuation coefficient .
However , the research work on discovering communities in multi-relational network ( MRN ) is still in its early stages . In this article , we have proposed a novel approach to extract the communities in a multi-relational network using DeepWalk network embedding technique and Consensus clustering . Empirical study is conducted on the real-world publicly available Twitter datasets .
Network embedding based on the random walk and skip-gram model such as the DeepWalk and Node2Vec algorithms have received wide attention . We identify that these algorithms essentially estimate the node similarities by random walk simulation , which is unreliable , inefficient , and inflexible .
However , vertices in lots of networks may contain community information or text contents , which could be good for relevant evaluation tasks , such as vertex classification , link prediction and so on . Since it has been proved that DeepWalk is actually equivalent to matrix factorization , we propose community and text-enhanced DeepWalk ( CTDW ) based on the inductive matrix completion algorithm , which incorporates community features and text features of vertices into NRL under the framework of matrix factorization . In experiments , we evaluate the proposed CTDW compared with other state-of-the-art methods on vertex classification .
This model learns low dimensional vectors to represent vertices appearing in a graph and , unlike existing work , integrates global structural information of the graph into the learning process . We also formally analyze the connections between our work and several previous research efforts , including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al. We conduct experiments on a language network , a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering , classification and visualization . Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks .
We first introduce the definition of serendipitous collaborators from three components of serendipity : relevance , unexpectedness , and value , respectively . Then we improve the transition probability of randomwalk in DeepWalk , and propose a serendipity-biased DeepWalk , called Seren2vec . The walker jumps to the next neighbor node with the proportional probability of edge weight in the co-author network .
DeepWalk & MFDW : For original random-walk based DeepWalk we set the window size to 5 . We also have MFDW aka NMF : S in Eqn : 3.2 the objective function for Matrix Factorized DeepWalk , as we build our model incrementally on top of it . Max-Margin DeepWalk ( MMDW ) : In this paper [ 6 ] , a max-margin loss is incorporated in the objective function of MFDW to learn discriminative representations of vertices .
Recently , attention has shifted to models that learn graph embeddings with methods inspired by the skip - gram model mikolov2013distributed . DeepWalk perozzi2014deepwalk learns embeddings via the prediction of the local neighborhood of nodes , sampled from random walks on the graph . LINE tang2015line and node2vec grovernode2vec extend DeepWalk with more sophisticated random walk or breadth - first search schemes .
Despite the recent progress , a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way . Here , we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network ( GIN ) , which was recently proposed as a state-of-the-art GNN for graph classification . One important observation in this paper is that the GIN is a realization of convolutional neural network ( CNN ) with two-tab filters in the graph space where the shift operation is realized using the adjacent matrix .
Abstract . The Swiss Joint Information Platform for Natural Hazards ( GIN ) has been realized from 2008 to 2010 as part of the Swiss federal government ’s OWARNA project , which aimed at optimizing warning and alerting procedures against natural hazard . The first online-version of the platform went productive in 2011 with the primary goal of providing measured and forecast natural hazard data in form of processed cartographic , graphic and other multimedia products to professional users – before , during and after natural hazard events .
User acceptance testing by means of surveys and direct user feedback sessions were key factors in this perennial redesign process . The GIN platform now features important novel technical and graphical elements : The starting page is based on a dashboard containing virtual dossiers ( Fig. 1 ) , with which users configure their desired information , data , and map bundles individually , or use predefined adaptable views on various existing data sets . In addition , there is a new overall spatial search function to query data parameters .
Stakeholders not only use GIN ’s ample database and cartographic product portfolio to accomplish their early warning and crisis management tasks , but also benefit from seamless , secure and reliable IT-services , provided by the Swiss Federal Government . With the new GIN platform , Switzerland has a powerful , integrative , and comprehensive tool for monitoring and responding to natural hazard events .
An ultrafast functional magnetic resonance imaging ( fMRI ) technique , called generalized inverse imaging ( GIN ) , is proposed , which combines inverse imaging with a phase constraint leading to a less underdetermined reconstruction and physiological noise correction . A single 3D echo planar imaging ( EPI ) prescan is sufficient to obtain the necessary coil sensitivity information and reference images that are used to reconstruct standard images , so that standard analysis methods are applicable .
Gamma Interconnection Network ( GIN ) is a type of Multistage Interconnection Network ( MIN ) that consists of layers of switching elements connected together in a predefined topology providing the connectivity between the set of processors and the set of memory modules . GIN is constructed by 1x3 , 3x3 and 3x1 switching elements .
By introducing co-occurrence relationship of commodities to explore the potential preferences , the weak generalization problem is also alleviated . To the best of our knowledge , the GIN method is the first to introduce graph learning for user intention mining in CTR prediction and propose end-to-end joint training of graph learning and CTR prediction tasks in sponsored search . At present , GIN has achieved excellent offline results on the real-world data of the e-commerce platform outperforming existing deep learning models , and has been running stable tests online and achieved significant CTR improvements .
This paper introduces the design process and the function of the GIN data collection system of grouting procedure and engineering database , using the technique of object-oriented user interface and the object-oriented programming design language Visual C++ 2 . 0 . This system implements perfectly the collection of the necessary data of grouting procedure . With the leading of the GIN theory which is in fashion currently , it implements the treatment and printout of the relative dato .
Specifically , a complementary AISEL dataset is generated , with labels actively acquired via a physics-based method to incorporate underlining physical knowledge at hand . An important component of our AISEL method is the bidirectional generative invertible network ( GIN ) , which can extract interpretable features from the training images and generate physically meaningful virtual images . Our AISEL method then efficiently samples virtual images not only further exploits the uncertain regions but also explores the entire image space .
The proposed framework can be used to assess and characterize individual disaster situations so that the GIN functional and design requirements can be accurately identified early . Future implications to GIN architecture are also discussed .
This paper introduces a user-friendly browser interface which integrates multilingual search and browsing functionalities within medical thesauri via the internet . The tool is being developed as part of the GIN Austria Patient Information System and is based on an adapted datamodel of the MeSH thesaurus . A prototype offers the possibility to build up queries and export lists of MeSH main headings collected during browsing the relevant MeSH trees .
The Martini Working Group is defining a mechanism for SIP IP-PBX type devices to REGISTER and obtain SIP service for E.164-based Address of Records , using the GIN mechanism defined in [ draft-gin ] . Two other drafts , [ draft-olive ] and [ draft-glass ] , propose the same for non-E.164-based AoRs .
However , existing network embedding methods mostly focus on unsigned networks and ignore the negative interactions between users . In this paper , we investigate the problem of learning representations for signed networks and present a novel deep network structure to incorporate both the balance and status theory in signed networks . With the proposed framework , we can simultaneously learn the node embedding encoding the status of a node and the edge embedding denoting the sign of an edge .
Comparing to other methods , the DPC method guarantees generating more robust representation points . Random mapping of the low-dimensional features in the deep embedding network guarantees the robustness of the method . The experimental results on real polarimetric SAR data demonstrate the effectiveness of the proposed method .
While a few deep neural networks have been adopted to capture the highly non-linearity , the deep structure makes them difficult to optimize in practice . In this paper , we propose a novel deep network embedding method , which exploits the fast learning speeds of extreme learning machine ( ELM ) . Particularly , we first design a deep ELM-based auto-encoder , based on which we then proposed an extended model to preserve both first-order and second-order proximities by a joint loss function .
Results We proposed a deep learning based method , DeepHE , to predict human essential genes by integrating features derived from sequence data and protein-protein interaction ( PPI ) network . A deep learning based network embedding method was utilized to automatically learn features from PPI network . In addition , 89 sequence features were derived from DNA sequence and protein sequence for each gene .
Abstract With the development of e-commerce , various methodologies have studied to improve recommendation performance . Recently , many deep learning based network embedding approaches are applied to the recommendation domain . However , these approaches still have several limitations , such as the problem of data sparseness and the changing in user preference over time , which can not be considered .
Therefore , how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem . To solve this problem , in this paper we propose a Structural Deep Network Embedding method , namely SDNE . More specifically , we first propose a semi-supervised deep model , which has multiple layers of non-linear functions , thereby being able to capture the highly non-linear network structure .
More specifically , we defined the molecular associations network and constructed a molecular associations dataset containing 105546 associations . Then , each node is represented by its attribute feature and network embedding learned by Structural Deep Network Embedding . Moreover , Random Forest is trained to predict any kind of associations .
Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes . A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability . To keep the local network neighborhood , we further perform a Laplacian supervised space optimization on the embedding feature representations .
section : Conclusion The main idea of the novel deep network architecture described in this paper involves increasing the feature map dimension gradually , in order to construct so - called PyramidNets along with the concept of ResNets . We also developed a novel residual unit , which includes a new building block for a residual unit with a zero - padded shortcut ; this design leads to significantly improved generalization ability .
The former includes surface normal constraints and information propagation along edges ; the latter includes perceptual features extracted from images as a guidance . We carefully design our network structure and propose a very deep cascaded graph convolutional neural network with “ shortcut ” connections . Meshes are progressively refined by our network trained end - to - end with the chamfer loss and normal loss .
Also related are conventional metric learning approaches that focus on learning a shallow ( linear ) Mahalanobis metric for a fixed feature representation . In contrast to prior work ’s fixed metric or fixed features and shallow learned metric , Relation Network can be seen as both learning a deep embedding and learning a deep non - linear metric ( similarity function ) . These are mutually tuned end - to - end to support each other in few short learning .
The emotion Contempt of the CKP set is not shown as no subject with consent for publication and an annotated emotion is part of the dataset . Results of experiments on these datasets demonstrate the success of using a deep layered neural network structure . With a 10 - fold cross - validation a recognition accuracy of 99.6 % has been achieved .
Our proposed xDeepFM achieves the best performance on all datasets , which demonstrates that combining explicit and implicit high - order feature interaction is necessary , and xDeepFM is effective in learning this class of combination . Another interesting observation is that , all the neural - based models do not require a very deep network structure for the best performance . Typical settings for the depth hyper - parameter are 2 and 3 , and the best depth setting for xDeepFM is 3 , which indicates that the interactions we learned are at most 4 - order .
[ Neural Variational Document Model ] [ Semantic Word Vector ] section : Details of the Deep Neural Network Structures subsection : Neural Variational Document Model
DeepWalk , LINE , and node2vec do not support external node / edge features . Our work is inspired by recent successful applications of autoencoder architectures for collaborative filtering that outperform popular matrix factorization methods , and is related to Structural Deep Network Embedding ( SDNE ) for link prediction . Similar to SDNE , our models rely on the autoencoder to learn non - linear node embeddings from local graph neighborhoods .
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness , i.e. , the random variation of their outcomes given identical algorithms and graphs . We apply five node embeddings algorithms --- HOPE , LINE , node2vec , SDNE , and GraphSAGE --- to synthetic and empirical graphs and assess their stability under randomness with respect to ( i ) the geometry of embedding spaces as well as ( ii ) their performance in downstream tasks . We find significant instabilities in the geometry of embedding spaces independent of the centrality of a node .
The usefulness of NEMII is further validated by case studies . The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction , and SDNE outperforms other popular network embedding methods : DeepWalk , High-Order Proximity preserved Embedding ( HOPE ) and Laplacian Eigenmaps (LE).ConclusionWe propose a new method , named NEMII , for predicting miRNA-disease associations , which has great potential to benefit the field of miRNA-disease association prediction .
In order to avoid the disadvantages of Quantuminspired shuffled frog leaping algorithm ( QSFLA ) , a fuzzy membership matrix with spatial information model is developed , which can remove isolated regions and further improve the detection accuracy . Segmentation , distribution and noise entropy ( SDNE ) model is also proposed to quantitatively evaluate the detection results . The detection results of the original sonar images demonstrate the effectiveness and adaptability of the proposed method .
In comparison to the baselines , we evaluate our MTGAE model on the same data splits over 10 runs with random weight initialization and report mean AUC / AP scores for link prediction and accuracy scores for node classification . We also compare the representation capacity of our MTGAE model against the related autoencoder - based SDNE model on the network reconstruction task . We use the ranking metric precision@ to evaluate the model
Results and Analysis Results of the reconstruction task for the Arxiv - GRQC and BlogCatalog network datasets are illustrated in Figure [ reference ] . In comparison to SDNE , we show that our MTGAE model achieves better precision@ performance for all values , up to for Arxiv - GRQC and for BlogCatalog , when trained on the complete datasets . We also systematically test the capacity of the MTGAE model to reconstruct the original networks when up to 80 percent of the edges are randomly removed , akin to the link prediction task .
Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data Graph Embedding based Wireless Link Scheduling with Few Training Samples Discovering Regularities from Traditional Chinese Medicine Prescriptions via Bipartite Embedding Model Learning Graph-based POI Embedding for Location-based Recommendation Joint Event-Partner Recommendation in Event-Based Social Networks The moral hazard of complexity-theoretic assumptions Polynomial time and space exact and heuristic algorithms for determining the generators, orbits and order of the graph automorphism group SemDiff: Finding Semtic Differences in Binary Programs based on Angr Clone Detection for Max/MSP Patch Libraries Neural Message Passing for Quantum Chemistry DeepWalk: online learning of social representations A Fast Approach of Graph Embedding Using Broad Learning System Do You Have a Pop Face? Here is a Pop Song. Using Profile Pictures to Mitigate the Cold-start Problem in Music Recommender Systems Improved DeepWalk Algorithm Based on Preference Random Walk Extracting Community Structure in Multi-relational Network via DeepWalk and Consensus Clustering Network Embedding Based on a Quasi-Local Similarity Measure Network Representation Learning Based on Community and Text Features GraRep: Learning Graph Representations with Global Structural Information A serendipity-biased Deepwalk for collaborators recommendation A Unified Non-Negative Matrix Factorization Framework for Semi Supervised Learning on Graphs Semi-Supervised Classification with Graph Convolutional Networks Understanding Graph Isomorphism Network for Brain MR Functional Connectivity Analysis The Swiss joint information platform for natural hazards On the application of ultra-fast fMRI and high resolution multiband fMRI at high static field strengths Reliability analysis of gamma interconnection network systems Graph Intention Network for Click-through Rate Prediction in Sponsored Search The Design and Implementation for An Object－Oriented Data Collection System of Grouting Procedure Active Image Synthesis for Efficient Labeling. A Framework for Designing a Global Information Network for Multinational Humanitarian Assistance/Disaster Relief A Multilingual Medical Thesaurus Browser for Patients and Medical Content Managers SIP Verification with Event-package for Resolution of Managed Open-ended Username Target Handles (VERMOUTH) BASSI: Balance and Status Combined Signed Network Embedding Unsupervised classification of polarimetric SAR images using deep embedding network Network embedding based on deep extreme learning machine DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning Item recommendation by predicting bipartite network embedding of user preference Structural Deep Network Embedding Construct a molecular associations network to systematically understand intermolecular associations in Human cells Network Structure and Transfer Behaviors Embedding via Deep Prediction Model Deep Pyramidal Residual Networks Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Learning to Compare: Relation Network for Few-Shot Learning DeXpression: Deep Convolutional Neural Network for Expression Recognition xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Neural Variational Inference for Text Processing Learning to Make Predictions on Graphs with Autoencoders The Effects of Randomness on the Stability of Node Embeddings A network embedding-based multiple information integration method for the MiRNA-disease association prediction Underwater Sonar Image Detection: A Novel Quantum-Inspired Shuffled Frog Leaping Algorithm Multi-Task Graph Autoencoders
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IJCAI CIKM ICDE ICML KDD RecSys Posters NLPCC (1) IHCI PRICAI (1) CCL CIKM SDM ICLR SIGIR MedInfo DASFAA (1) KDD AAAI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv KDD ICML 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) ArXiv
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