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

Abstract

Structural pattern analysis is of fundamental importance as it provides a novel perspective on illustration of the rela tionship 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 net works 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 structura l 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.