AOC; Assembling Overlapping Communities

TL;DR

AOC is a meta-method designed to identify overlapping communities in large networks, addressing the limitations of disjoint clustering methods, demonstrated on a vast biological research network.

Contribution

AOC introduces a novel approach for detecting overlapping communities, complementing existing disjoint clustering techniques, and is scalable to networks with over 13 million nodes.

Findings

Successfully applied AOC to a network of over 13 million nodes.

Enabled detection of overlapping communities in large-scale biological research data.

Addresses limitations of disjoint clustering in community detection.

Abstract

Through discovery of meso-scale structures, community detection methods contribute to the understanding of complex networks. Many community finding methods, however, rely on disjoint clustering techniques, in which node membership is restricted to one community or cluster. This strict requirement limits the ability to inclusively describe communities since some nodes may reasonably be assigned to many communities. We have previously reported Iterative K-core Clustering (IKC), a scalable and modular pipeline that discovers disjoint research communities from the scientific literature. We now present Assembling Overlapping Clusters (AOC), a complementary meta-method for overlapping communities as an option that addresses the disjoint clustering problem. We present findings from the use of AOC on a network of over 13 million nodes that captures recent research in the very rapidly growing field of extracellular vesicles in biology.