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A novel cosine distance for detecting communities in complex networks

Author

Listed:
  • Wang, Tao
  • Wang, Hongjue
  • Wang, Xiaoxia

Abstract

Detecting communities is significant to understand the potential structures and functions of complex systems. In order to detect communities more accurately and reasonably, a novel algorithm is proposed based on cosine distance and core-node in this paper. Cosine distances between nodes are regarded as their similarity measure and network node vectors can be extracted directly from the similarity matrix without calculating eigenvectors. Core-nodes as the initial communities are found by cosine distance threshold and degree threshold. Furthermore, the initial communities are expanded by adding other nodes with the nearest cosine distance to core-nodes. Through changing degree and cosine distance thresholds constantly, the optimal community structure of complex networks can be obtained by optimizing modularity with high accuracy. Experimental results on both real-world and synthetic networks demonstrate the feasibility and effectiveness of the proposed algorithm.

Suggested Citation

  • Wang, Tao & Wang, Hongjue & Wang, Xiaoxia, 2015. "A novel cosine distance for detecting communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 21-35.
  • Handle: RePEc:eee:phsmap:v:437:y:2015:i:c:p:21-35
    DOI: 10.1016/j.physa.2015.05.101
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    References listed on IDEAS

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    1. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Overlapping community detection using neighborhood ratio matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 510-521.
    2. Li, Junqiu & Wang, Xingyuan & Eustace, Justine, 2013. "Detecting overlapping communities by seed community in weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6125-6134.
    3. Cui, Yaozu & Wang, Xingyuan & Li, Junqiu, 2014. "Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 85-91.
    4. James Moody & Douglas R. White, 2000. "Structural Cohesion and Embeddedness: A Hierarchical Conception of Social Groups," Working Papers 00-08-049, Santa Fe Institute.
    5. Eustace, Justine & Wang, Xingyuan & Cui, Yaozu, 2015. "Community detection using local neighborhood in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 665-677.
    6. Cui, Yaozu & Wang, Xingyuan & Eustace, Justine, 2014. "Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 198-207.
    7. Wang, Xingyuan & Li, Junqiu, 2013. "Detecting communities by the core-vertex and intimate degree in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2555-2563.
    8. Cui, Yaozu & Wang, Xingyuan, 2014. "Uncovering overlapping community structures by the key bi-community and intimate degree in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 7-14.
    9. Li, Junqiu & Wang, Xingyuan & Cui, Yaozu, 2014. "Uncovering the overlapping community structure of complex networks by maximal cliques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 398-406.
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    1. repec:eee:phsmap:v:490:y:2018:i:c:p:1344-1354 is not listed on IDEAS

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