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Partitioning of Hypergraph Modeled Complex Networks Based on Information Entropy

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Algorithms and Architectures for Parallel Processing (ICA3PP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9529))

Abstract

Complex networks with nonuniform degree distribution characteristics are called scale-free networks, which can be divided into several natural imbalanced communities. Hypergraph is good at modeling complex networks, and balanced partitioning. But traditional hypergraph partitioning tools with balance constraints could not achieve good partitioning results for nature imbalanced datasets. In order to partition a complex network into “natural” structure, and reduce the inter-part communication cost simultaneously, we make three contributions in this paper. First, we use an information entropy expression considering degree distribution to describe the complex networks. Second, we put forward a partitioning tool named EQHyperpart, which uses complex network information Entropy based modularity Q to direct the partitioning process. Finally, evaluation tests are performed on modern scale-free networks and some classical real world datasets. Experimental results show that EQHyperpart can achieve a tradeoff between modularity retaining and cut size minimizing of hypergraph modeled complex networks.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grant Numbers 61272151, 61472451, and 61402543, the International Science & Technology Cooperation Program of China under Grant Number 2013DFB10070, the China Hunan Provincial Science & Technology Program under Grant Number 2012GK4106, and the “Mobile Health" Ministry of Education - China Mobile Joint Laboratory (MOE-DST No. [2012]311).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Wenyin Yang, Guojun Wang & Md Zakirul Alam Bhuiyan

  2. School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China

    Wenyin Yang

  3. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, 19122, USA

    Md Zakirul Alam Bhuiyan

  4. School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, China

    Guojun Wang

Authors
  1. Wenyin Yang
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  2. Guojun Wang
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  3. Md Zakirul Alam Bhuiyan
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Corresponding author

Correspondence to Guojun Wang .

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Editors and Affiliations

  1. Central South University, Changsha, China

    Guojun Wang

  2. The University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. University of Murcia, Murcia, Murcia, Spain

    Gregorio Martinez

  4. Hunan University , Changsha, China

    Kenli Li

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Yang, W., Wang, G., Bhuiyan, M.Z.A. (2015). Partitioning of Hypergraph Modeled Complex Networks Based on Information Entropy. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_47

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  • DOI: https://doi.org/10.1007/978-3-319-27122-4_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27121-7

  • Online ISBN: 978-3-319-27122-4

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