A survey on hierarchical community detection in large-scale complex networks

Document Type : Review Article


College of Engineering and Computer Science, Australian National University, Canberra, Australia


Vertices in a real-world social network can be grouped into densely connected communities that are sparsely connected to other groups, and these com[1]munities can be partitioned into successively more cohesive communities. Given the ever-growing pile of research on community detection, various researchers have surveyed the evolution of various community detection methods such as flat community detection, overlapping community detection, dynamic community detection and community search. Yet, the problem of hierarchical community detection, despite being well studied, has not been surveyed and the evolution of methods to identify hierarchies of communities in large-scale complex networks has not been documented. In this survey, we study the hierarchical community detection problem and formally define this problem. We then classify the existing works on hierarchical community detection and discuss some of the flat community detection approaches that are capable of producing hierarchies. We then introduce a set of empirical analysis tools, such as benchmark datasets and accuracy measures to evaluate the performance of a hierarchical community detection method.


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