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dc.contributor.authorPeng, Chengbin
dc.contributor.authorWong, Ka Chun
dc.date.accessioned2015-08-11T13:44:17Z
dc.date.available2015-08-11T13:44:17Z
dc.date.issued2014-12
dc.identifier.citationPeng, C., & Wong, K.-C. (2014). Exploring Mixed Membership Stochastic Block Models via Non-negative Matrix Factorization. 2014 IEEE International Conference on Data Mining Workshop. doi:10.1109/icdmw.2014.124
dc.identifier.doi10.1109/ICDMW.2014.124
dc.identifier.urihttp://hdl.handle.net/10754/565871
dc.description.abstractMany real-world phenomena can be modeled by networks in which entities and connections are represented by nodes and edges respectively. When certain nodes are highly connected with each other, those nodes forms a cluster, which is called community in our context. It is usually assumed that each node belongs to one community only, but evidences in biology and social networks reveal that the communities often overlap with each other. In other words, one node can probably belong to multiple communities. In light of that, mixed membership stochastic block models (MMB) have been developed to model those networks with overlapping communities. Such a model contains three matrices: two incidence matrices indicating in and out connections and one probability matrix. When the probability of connections for nodes between communities are significantly small, the parameter inference problem to this model can be solved by a constrained non-negative matrix factorization (NMF) algorithm. In this paper, we explore the connection between the two models and propose an algorithm based on NMF to infer the parameters of MMB. The proposed algorithms can detect overlapping communities regardless of knowing or not the number of communities. Experiments show that our algorithm can achieve a better community detection performance than the traditional NMF algorithm. © 2014 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleExploring Mixed Membership Stochastic Block Models via Non-negative Matrix Factorization
dc.typeConference Paper
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journal2014 IEEE International Conference on Data Mining Workshop
dc.conference.date14 December 2014
dc.conference.name14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
kaust.personPeng, Chengbin


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