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    Exploring Mixed Membership Stochastic Block Models via Non-negative Matrix Factorization

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    Type
    Conference Paper
    Authors
    Peng, Chengbin cc
    Wong, Ka Chun
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2014-12
    Permanent link to this record
    http://hdl.handle.net/10754/565871
    
    Metadata
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    Abstract
    Many 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.
    Citation
    Peng, 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
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2014 IEEE International Conference on Data Mining Workshop
    Conference/Event name
    14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
    DOI
    10.1109/ICDMW.2014.124
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDMW.2014.124
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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