Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks

TL;DR

This paper introduces a scalable overlapping community detection method that identifies both cohesive and 2-mode communities in directed and undirected networks, accounting for edge directions and complex connectivity patterns.

Contribution

It extends traditional community definitions to include 2-mode communities and incorporates edge directedness, enabling detection of diverse community structures in large networks.

Findings

Successfully detects overlapping cohesive and 2-mode communities

Handles directed and undirected networks effectively

Scales to networks with millions of nodes and edges

Abstract

Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks. In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.