A Modular Multiscale Approach to Overlapping Community Detection

TL;DR

This paper introduces a modular, multiscale method for detecting overlapping communities in social networks, emphasizing different scales of community structure and enabling efficient, adaptable algorithms.

Contribution

It presents a novel, scalable, and modular methodology for overlapping community detection that can be tailored to specific community definitions across multiple scales.

Findings

The algorithm is computationally efficient for large, sparse networks.

It naturally allows nodes to belong to multiple communities.

The method is highly adaptable to different community notions.

Abstract

In this work we address the problem of detecting overlapping communities in social networks. Because the word "community" is an ambiguous term, it is necessary to quantify what it means to be a community within the context of a particular type of problem. Our interpretation is that this quantification must be done at a minimum of three scales. These scales are at the level of: individual nodes, individual communities, and the network as a whole. Each of these scales involves quantitative features of community structure that are not accurately represented at the other scales, but are important for defining a particular notion of community. Our work focuses on providing sensible ways to quantify what is desired at each of these scales for a notion of community applicable to social networks, and using these models to develop a community detection algorithm. Appealing features of our approach is that it naturally allows for nodes to belong to multiple communities, and is computationally efficient for large networks with low overall edge density. The scaling of the algorithm is $O(N~\overline{k^2} + \overline{N_{com}^2})$, where $N$ is the number of nodes in the network, $\overline{N_{com}^2}$ is the average squared community size, and $\overline{k^2}$ is the expected value of a node's degree squared. Although our work focuses on developing a computationally efficient algorithm for overlapping community detection in the context of social networks, our primary contribution is developing a methodology that is highly modular and can easily be adapted to target specific notions of community.