Overlapping Network Community Detection Using Sparse Backbones
TL;DR
This work proposes Highway, a scalable OCD algorithm that exploits the sparse backbone of the input network to perform efficient community inference and shows competitive performance for Highway, which ranks first in overlapping normalized mutual information and ranks second in all the other four performance measures.
Abstract
Community structures are common in real networks, and extracting them provides valuable insight in applications ranging from drug discovery to market segmentation. Overlapping community detection (OCD) is the task of clustering networked data in which nodes may belong to multiple clusters. Existing OCD algorithms often struggle to achieve a suitable balance between detection quality and scalability. We, therefore, propose Highway, a scalable OCD algorithm that exploits the sparse backbone of the input network to perform efficient community inference. We used 728 Lancichinetti-Fortunato-Radicchi benchmark networks to compare Highway and its ablated version against 10 existing OCD algorithms. Our results, based on five performance measures, demonstrate a competitive performance for Highway. It ranks first in overlapping normalized mutual information with a 6.9% improvement over the strongest baseline. It also ranks second in all the other four performance measures. These comparative results suggest that Highway coupled with its backbone procedure offers a suitable accuracy-efficiency trade-off. The Highway algorithm is open-source and available as part of the CDlib library.