Accurately Detecting Community with Large Attribute in Partial Networks

A community structure is the most significant feature of social networks. Fusing the relation information and the attribute information to is necessary to detect community in the attributed social network. However, both relation and attribute information will have non-uniform quality because of the meaningless or erroneous noise in a social network. Moreover, the nodes that lose relation or attribute information will make the network into a partial network. In those cases, it is unrealistic to split users into different communities correctly without considering the noise and incompleteness in combination processing. To solve this problem, we propose a non-negative matrix factorization (NMF)-based community detection framework. In this framework, common and correct community structures can be identified effectively and disagreements can be reconciled by introducing two regularizations in combination processing. The experimental results confirm the superior performance of the method and demonstrate its effectiveness for a partial network.

[1]  Inderjit S. Dhillon,et al.  Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion , 2015, IEEE Transactions on Knowledge and Data Engineering.

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

[3]  Xiao Liu,et al.  Community detection enhancement using non-negative matrix factorization with graph regularization , 2016 .

[4]  Xiaochun Cao,et al.  Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network , 2017, Scientific Reports.

[5]  Dal Yong Jin,et al.  The social mediascape of transnational Korean pop culture: Hallyu 2.0 as spreadable media practice , 2016, New Media Soc..

[6]  Xiaochun Cao,et al.  Semantic Community Identification in Large Attribute Networks , 2016, AAAI.

[7]  Charu C. Aggarwal,et al.  Negative Link Prediction in Social Media , 2014, WSDM.

[8]  Yike Guo,et al.  Fast graph clustering with a new description model for community detection , 2017, Inf. Sci..

[9]  Jure Leskovec,et al.  Community Detection in Networks with Node Attributes , 2013, 2013 IEEE 13th International Conference on Data Mining.

[10]  Zhao Kang,et al.  Integrating feature and graph learning with low-rank representation , 2017, Neurocomputing.

[11]  Hasan Davulcu,et al.  Community detection in political Twitter networks using Nonnegative Matrix Factorization methods , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[12]  Hong Cheng,et al.  A model-based approach to attributed graph clustering , 2012, SIGMOD Conference.

[13]  Barbora Micenková,et al.  Clustering attributed graphs: Models, measures and methods , 2015, Network Science.

[14]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

[15]  Katia P. Sycara,et al.  Nonnegative Matrix Tri-Factorization with Graph Regularization for Community Detection in Social Networks , 2015, IJCAI.

[16]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[17]  Fei Wang,et al.  Community discovery using nonnegative matrix factorization , 2011, Data Mining and Knowledge Discovery.

[18]  Xiaoke Ma,et al.  Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability , 2017, Pattern Recognit..

[19]  Rich Ling,et al.  “It’s just not that exciting anymore”: The changing centrality of SMS in the everyday lives of young Danes , 2016, New Media Soc..

[20]  Weidi Dai,et al.  A multi-similarity spectral clustering method for community detection in dynamic networks , 2016, Scientific Reports.

[21]  Derek Greene,et al.  Producing a unified graph representation from multiple social network views , 2013, WebSci.

[22]  Yu Zhou,et al.  Nonnegative matrix factorization with mixed hypergraph regularization for community detection , 2018, Inf. Sci..

[23]  Mason A. Porter,et al.  Random walks and diffusion on networks , 2016, ArXiv.

[24]  Lei Du,et al.  Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition , 2014, AAAI.

[25]  Weixiong Zhang,et al.  Modeling with Node Degree Preservation Can Accurately Find Communities , 2015, AAAI.

[26]  Philip S. Yu,et al.  Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization , 2015, ECML/PKDD.

[27]  Reynold Cheng,et al.  Effective Community Search for Large Attributed Graphs , 2016, Proc. VLDB Endow..

[28]  Thomas S. Huang,et al.  Graph Regularized Nonnegative Matrix Factorization for Data Representation. , 2011, IEEE transactions on pattern analysis and machine intelligence.

[29]  Jing Hua,et al.  Non-negative matrix factorization for semi-supervised data clustering , 2008, Knowledge and Information Systems.

[30]  Hong Cheng,et al.  Dense community detection in multi-valued attributed networks , 2015, Inf. Sci..

[31]  Charu C. Aggarwal,et al.  Recommendations in Signed Social Networks , 2016, WWW.

[32]  Kyumin Lee,et al.  Campaign extraction from social media , 2013, ACM Trans. Intell. Syst. Technol..

[33]  Feiping Nie,et al.  Clustering and projected clustering with adaptive neighbors , 2014, KDD.

[34]  Dong Zhou,et al.  Translation techniques in cross-language information retrieval , 2012, CSUR.

[35]  Diego Garlaschelli,et al.  Ground truth? Concept-based communities versus the external classification of physics manuscripts , 2016, EPJ Data Science.

[36]  Liang Tang,et al.  Enhancing community detection by using local structural information , 2016, ArXiv.

[37]  Hui Xiong,et al.  Introduction to special section on intelligent mobile knowledge discovery and management systems , 2013, ACM Trans. Intell. Syst. Technol..

[38]  Xiao Liu,et al.  Semi-supervised community detection based on non-negative matrix factorization with node popularity , 2017, Inf. Sci..