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Discovering Mesoscopic-level Structural Patterns on Social Networks: A Node-similarity Perspective |
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PP: 317-335 |
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Author(s) |
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Qing Cheng,
Zhong Liu,
Jincai Huang,
Guangquan Cheng,
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Abstract |
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| 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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