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A scanning method for detecting clustering pattern of both attribute and structure in social networks

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  • Wang, Tai-Chi
  • Phoa, Frederick Kin Hing

Abstract

Community/cluster is one of the most important features in social networks. Many cluster detection methods were proposed to identify such an important pattern, but few were able to identify the statistical significance of the clusters by considering the likelihood of network structure and its attributes. Based on the definition of clustering, we propose a scanning method, originated from analyzing spatial data, for identifying clusters in social networks. Since the properties of network data are more complicated than those of spatial data, we verify our method’s feasibility via simulation studies. The results show that the detection powers are affected by cluster sizes and connection probabilities. According to our simulation results, the detection accuracy of structure clusters and both structure and attribute clusters detected by our proposed method is better than that of other methods in most of our simulation cases. In addition, we apply our proposed method to some empirical data to identify statistically significant clusters.

Suggested Citation

  • Wang, Tai-Chi & Phoa, Frederick Kin Hing, 2016. "A scanning method for detecting clustering pattern of both attribute and structure in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 295-309.
    • Handle: RePEc:eee:phsmap:v:445:y:2016:i:c:p:295-309
    DOI: 10.1016/j.physa.2015.10.009
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    Cited by:

    1. Frederick Kin Hing Phoa & Hsin-Yi Lai & Livia Lin-Hsuan Chang & Keisuke Honda, 2020. "A two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 851-863, November.

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