TY - GEN

T1 - Exploring Mixed Membership Stochastic Block Models via Non-negative Matrix Factorization

AU - Peng, Chengbin

AU - Wong, Ka Chun

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2014/12

Y1 - 2014/12

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/565871

UR - http://ieeexplore.ieee.org/document/7022691/

UR - http://www.scopus.com/inward/record.url?scp=84936865961&partnerID=8YFLogxK

U2 - 10.1109/ICDMW.2014.124

DO - 10.1109/ICDMW.2014.124

M3 - Conference contribution

SN - 9781479942749

SP - 892

EP - 897

BT - 2014 IEEE International Conference on Data Mining Workshop

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -