Network Representation Learning Using Local Sharing and Distributed Matrix Factorization (LSDMF)

Vector embedding over a real network is considered as feature learning of nodes of the network which is utilized in many downstream machine learning applications such as link prediction. A network of size n can be represented as a collection of n vectors (feature vectors) of dimension d (≪ n) which have encoded structural and spectral information of the associated network. These feature vectors can be used in two ways: first, in the extraction of existing links and other higher order structural or functional relations among the nodes of the network and second, in the prediction of the structural evolution of the network in near future. It is observed that matrix factorization based vector embedding algorithms are able to learn more informative feature vectors but scalability is a major bottleneck due to memory and computationally intensive task.

[1]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[2]  Pradumn Kumar Pandey,et al.  A Parametric Model Approach for Structural Reconstruction of Scale-Free Networks , 2017, IEEE Transactions on Knowledge and Data Engineering.

[3]  M. Newman.  Resource Letter CS-1: Complex Systems , 2011 .

[4]  D. Bu,et al.  Topological structure analysis of the protein-protein interaction network in budding yeast. , 2003, Nucleic acids research.

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

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

[7]  Niloy Ganguly,et al.  Discriminative Link Prediction using Local, Community, and Global Signals , 2016, IEEE Transactions on Knowledge and Data Engineering.

[8]  Pradumn Kumar Pandey,et al.  Context dependent preferential attachment model for complex networks , 2015, ArXiv.

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

[10]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[12]  Omer Levy,et al.  Dependency-Based Word Embeddings , 2014, ACL.

[13]  Padhraic Smyth,et al.  A Spectral Clustering Approach To Finding Communities in Graph , 2005, SDM.

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

[15]  A. Arenas,et al.  Models of social networks based on social distance attachment. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[17]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[18]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[19]  Stefan Bornholdt,et al.  Handbook of Graphs and Networks: From the Genome to the Internet , 2003 .

[20]  John Scott.  What is social network analysis , 2010 .

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

[22]  Pradumn Kumar Pandey,et al.  Reconstruction of network topology using status-time-series data , 2018 .

[23]  Alexander J. Smola,et al.  Distributed large-scale natural graph factorization , 2013, WWW.

[24]  A Díaz-Guilera,et al.  Self-similar community structure in a network of human interactions. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Frédéric Cazals,et al.  A note on the problem of reporting maximal cliques , 2008, Theor. Comput. Sci..