Node-Centric Detection of Overlapping Communities in Social Networks

TL;DR

NECTAR is a novel community detection algorithm that adapts its optimization strategy for overlapping communities, demonstrating superior performance on synthetic and real-world social network data.

Contribution

It introduces a dynamic objective function selection in a Louvain-inspired framework for overlapping community detection.

Findings

NECTAR outperforms existing algorithms on benchmark graphs.

It effectively detects overlapping communities in real-world networks.

The method adapts to different network structures for optimal results.

Abstract

We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.