A Double Non-Negative Matrix Factorization Model for Signed Network Analysis

As one of the most important characteristics of complex networks, community structure have been studied extensively in real cases. However, in signed networks that include both positive and negative edges, the development of community discovery is still limited now. Because the sign information on edges poses a challenge to modeling the signed network. Most existing methods are based on heuristics, so these methods tend to have high computational complexity and ignore the generation of the networks. Here, we propose a Double Non-negative Matrix Factorization (DouNMF) model from the perspective of generative model to detect communities in the signed network. This algorithm skillfully applies the Non-negative matrix Factorization algorithm to the signed network. In addition, the algorithm integrates indegree information into the process of matrix factorization. Large amounts of experiments on several artificial and real-world signed networks validate that the effectiveness and accuracy of our proposed approaches both in community discovery and link prediction.

[1]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

[2]  Ernestina Menasalvas Ruiz,et al.  Combining complex networks and data mining: why and how , 2016, bioRxiv.

[3]  Talel Abdessalem,et al.  Casting a web of trust over Wikipedia: an interaction-based approach , 2011, WWW.

[4]  Wei Yu,et al.  Exploring temporal community structure and constant evolutionary pattern hiding in dynamic networks , 2018, Neurocomputing.

[5]  F. Heider.  Attitudes and cognitive organization. , 1946, The Journal of psychology.

[6]  Jing Liu,et al.  A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks , 2013, Soft Computing.

[7]  K. E. Read,et al.  Cultures of the Central Highlands, New Guinea , 1954, Southwestern Journal of Anthropology.

[8]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[9]  Charu C. Aggarwal,et al.  A Survey of Signed Network Mining in Social Media , 2015, ACM Comput. Surv..

[10]  L. Freeman.  Research Methods in Social Network Analysis , 1991 .

[11]  Buzhou Tang,et al.  Overlapping community detection in networks with positive and negative links , 2014 .

[12]  Christos Faloutsos,et al.  REV2: Fraudulent User Prediction in Rating Platforms , 2018, WSDM.

[13]  Bo Yang,et al.  Stochastic Blockmodeling and Variational Bayes Learning for Signed Network Analysis , 2017, IEEE Transactions on Knowledge and Data Engineering.

[14]  Xuehua Zhao,et al.  Statistical inference for community detection in signed networks. , 2017, Physical review. E.

[15]  Minyi Guo,et al.  SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction , 2017, WSDM.

[16]  Nagarajan Natarajan,et al.  Prediction and clustering in signed networks: a local to global perspective , 2013, J. Mach. Learn. Res..

[17]  Pengfei Jiao,et al.  Link predication based on matrix factorization by fusion of multi class organizations of the network , 2017, Scientific Reports.

[18]  Jie Zhang,et al.  FILE: A Novel Framework for Predicting Social Status in Signed Networks , 2018, AAAI.

[19]  Jonathan Qiang Jiang,et al.  Stochastic Blockmodel and Exploratory Analysis in Signed Networks , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Mark E. J. Newman,et al.  Community detection in networks: Modularity optimization and maximum likelihood are equivalent , 2016, ArXiv.

[21]  Charu C. Aggarwal,et al.  Attributed Signed Network Embedding , 2017, CIKM.

[22]  Dongjin Song,et al.  Recommending Positive Links in Signed Social Networks by Optimizing a Generalized AUC , 2015, AAAI.

[23]  Clara Pizzuti,et al.  An Evolutionary and Local Refinement Approach for Community Detection in Signed Networks , 2016, Int. J. Artif. Intell. Tools.

[24]  Malik Magdon-Ismail,et al.  Communities and Balance in Signed Networks: A Spectral Approach , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[25]  M. Newman.  Community detection in networks: Modularity optimization and maximum likelihood are equivalent , 2016, Physical review. E.

[26]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[27]  Andrej Mrvar,et al.  An Analysis of the Slovene Parliamentary Parties Network , 2003 .