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01-Applied Mathematics & Information Sciences
An International Journal


Volumes > Volume 9 > No. 1


Discovering Mesoscopic-level Structural Patterns on Social Networks: A Node-similarity Perspective

PP: 317-335
Qing Cheng, Zhong Liu, Jincai Huang, Guangquan Cheng,
Structural pattern analysis is of fundamental importance as it provides a novel perspective on illustration of the relationship between structure and function, as well as to understand the dynamics, of social networks. So far, scientists have uncovered a multitude of structural patterns ubiquitously existing in social networks in different levels, they may be microscopic, mesoscopic or macroscopic. Our work mainly characterizes the mesoscopic-level structural patterns on social networks from the node-similarity viewpoint and reviews some latest representative methods, focusing on the improved methods of community measure and community structure detection, role discovery methods, as well as the structural group discovery approaches used to reveal hidden but unambiguous structures. Finally, we also outline some important open problems, which may be valuable for related research domains.

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