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Community Inference with Bayesian Non-negative Matrix Factorization

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9931))

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Abstract

In terms of networks, the clustering is based on the topology structure of the network and the groups found are called Communities. We might expect a coherent group to be one which has more links between members of the group than it has to nodes outside the group in other clusters. Detection Communities in a large network can efficiently simplify network structure, help to understand the network topology and learn how the network works.

As a dimension reduction method, Non-negative Matrix Factorization (NMF) aims to find two non-negative matrices whose product approximates the original matrix well, and is widely used in graph clustering condition with good physical interpretability and universal applicability. Based on the consideration that there is no any physical meaning to reconstruct a network with negative adjacency matrix, using NMF to obtain new representations of network with non-negativity constraints can achieve much productive effect in community analysis.

Incorporating Bayesian methods with prior knowledge for NMF, we can gain further insights into the data and determinate the optimal parameters for detecting model. In this paper, we propose a Bayesian non-negative matrix factorization method with Symmetric assumption (BSNMF), which not only achieve better community detection results in undirected network, but also effectively predict most appropriate count of communities in a large network with Automatic Relevance Determination model. We compare our approaches with other NMF-based methods in Email social networks, and experimental results for community detection show that our approaches are effective to find the communities number and achieve better community detection results.

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Notes

  1. 1.

    http://deim.urv.cat/~alexandre.arenas/.

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Acknowledgments

This work was supported by NSFC (no. 61272247), the Science and Technology Commission of Shanghai Municipality (Grant No. 13511500200), the Arts and Science Cross Special Fund of Shanghai Jiao Tong University under Grant 13JCY14, the European Union Seventh Framework Programme (Grant NO. 247619).

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Authors and Affiliations

  1. MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, Shanghai, China

    Xiaohua Shi & Hongtao Lu

  2. Library, Shanghai Jiaotong University, Shanghai, China

    Xiaohua Shi

Authors
  1. Xiaohua Shi
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  2. Hongtao Lu
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Correspondence to Xiaohua Shi .

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Editors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, Utah, USA

    Feifei Li

  2. School of Electrical Engineering, Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. Soochow University , Suzhou, China

    Kai Zheng

  4. Soochow University , Suzhou, China

    Guanfeng Liu

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Shi, X., Lu, H. (2016). Community Inference with Bayesian Non-negative Matrix Factorization. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-45814-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45813-7

  • Online ISBN: 978-3-319-45814-4

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