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Using Motif to Characterize and Analyze Mesoscopic Features of Social Networks

  

  1. College of History and Culture, Sichuan University, Chengdu 610200, China
  • Received:2016-12-09 Online:2017-08-31 Published:2017-09-01

Abstract:

Motif is an important mesoscopic structure which exists in networks. Motif discovery is a key problem in data analysis of social networks. By studying the existence of
motif in social networks topology structure， the evolution of networks can be understood better. The RandESU algorithm was used in motif detection and motif features
analysis. The difference of motifs features in social networks, power networks and Internet was compared and analyzed. The experimental result shows that social networks have
greater probabilities to form the structure of “triadic closure”. They have stronger community features and bigger clustering coefficients. And, nodes also have a tendency to
connect with the hubs nodes. We also find that although the number of nodes occupy “structural holes” is less, but it plays a key role in the information propagation. Through
research an important conclusions can be drawled: “preferential attachment” and “triadic closure” are the reasons for driving network evolution.

Key words: social networks, evolution of networks, motif, triadic closure, preferential attachment

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