Dynamic Joint Variational Graph Autoencoders

Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for static graphs mainly and cannot capture the evolution of a large dynamic network. In this paper, we propose Dynamic joint Variational Graph Autoencoders (Dyn-VGAE) that can learn both local structures and temporal evolutionary patterns in a dynamic network. Dyn-VGAE provides a joint learning framework for computing temporal representations of all graph snapshots simultaneously. Each auto-encoder embeds a graph snapshot based on its local structure and can also learn temporal dependencies by collaborating with other autoencoders. We conduct experimental studies on dynamic real-world graph datasets and the results demonstrate the effectiveness of the proposed method.

[1]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[2]  Kevin Chen-Chuan Chang,et al.  A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.

[3]  Jure Leskovec,et al.  {SNAP Datasets}: {Stanford} Large Network Dataset Collection , 2014 .

[4]  Feiran Huang,et al.  Multimodal Network Embedding via Attention based Multi-view Variational Autoencoder , 2018, ICMR.

[5]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[6]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[7]  Ron Meir,et al.  Joint Autoencoders: A Flexible Meta-learning Framework , 2018, ECML/PKDD.

[8]  Wei Lu,et al.  Deep Neural Networks for Learning Graph Representations , 2016, AAAI.

[9]  Palash Goyal,et al.  Capturing Edge Attributes via Network Embedding , 2018, IEEE Transactions on Computational Social Systems.

[10]  Aijun An,et al.  dynnode2vec: Scalable Dynamic Network Embedding , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[11]  Aram Galstyan,et al.  Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (Extended Abstract) , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[12]  Yueting Zhuang,et al.  Dynamic Network Embedding by Modeling Triadic Closure Process , 2018, AAAI.

[13]  Junjie Wu,et al.  Embedding Temporal Network via Neighborhood Formation , 2018, KDD.

[14]  Alan L. Yuille,et al.  Joint Image-Text Representation by Gaussian Visual-Semantic Embedding , 2016, ACM Multimedia.

[15]  Yan Liu,et al.  DynGEM: Deep Embedding Method for Dynamic Graphs , 2018, ArXiv.

[16]  Max Welling,et al.  Variational Graph Auto-Encoders , 2016, ArXiv.

[17]  Cheng Deng,et al.  Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[18]  Lina Yao,et al.  Adversarially Regularized Graph Autoencoder , 2018, IJCAI.

[19]  Qiongkai Xu,et al.  GraRep: Learning Graph Representations with Global Structural Information , 2015, CIKM.

[20]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[21]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..

[22]  Marc'Aurelio Ranzato,et al.  Large Scale Distributed Deep Networks , 2012, NIPS.

[23]  Jure Leskovec,et al.  Learning Dynamic Embeddings from Temporal Interactions , 2018, ArXiv.

[24]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[25]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[26]  Mohak Shah,et al.  Usability Study of Distributed Deep Learning Frameworks For Convolutional Neural Networks , 2018 .

[27]  Zhiyuan Liu,et al.  Max-Margin DeepWalk: Discriminative Learning of Network Representation , 2016, IJCAI.

[28]  Juan-Zi Li,et al.  Extraction and mining of an academic social network , 2008, WWW.

[29]  Palash Goyal,et al.  DynamicGEM: A Library for Dynamic Graph Embedding Methods , 2018, ArXiv.

[30]  Jian Pei,et al.  Community Preserving Network Embedding , 2017, AAAI.

[31]  A. Moore,et al.  Dynamic social network analysis using latent space models , 2005, SKDD.

[32]  Jian Pei,et al.  Asymmetric Transitivity Preserving Graph Embedding , 2016, KDD.

[33]  Jure Leskovec,et al.  Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..

[34]  Aram Galstyan,et al.  Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization , 2014, ArXiv.

[35]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[36]  Philip S. Yu,et al.  Embedding of Embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks , 2017, WSDM.

[37]  Hongyuan Zha,et al.  Representation Learning over Dynamic Graphs , 2018, ArXiv.

[38]  Ryan A. Rossi,et al.  Continuous-Time Dynamic Network Embeddings , 2018, WWW.

[39]  Charu C. Aggarwal,et al.  NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks , 2018, KDD.

[40]  Jian Pei,et al.  A Survey on Network Embedding , 2017, IEEE Transactions on Knowledge and Data Engineering.

[41]  Jie Tang,et al.  ArnetMiner: extraction and mining of academic social networks , 2008, KDD.

[42]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[43]  Tao Mei,et al.  Jointly Modeling Embedding and Translation to Bridge Video and Language , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).