DyPerm: Maximizing Permanence for Dynamic Community Detection

TL;DR

DyPerm is a novel dynamic community detection method that maximizes a new metric called permanence, offering high accuracy and efficiency by updating community structures incrementally in evolving networks.

Contribution

It introduces DyPerm, the first method to optimize permanence in dynamic networks, with theoretical guarantees and significant improvements in speed and accuracy.

Findings

Achieves 35% higher accuracy (NMI) than baseline methods.

15 times faster than static community detection methods.

Effective on both synthetic and real-world networks.

Abstract

In this paper, we propose DyPerm, the first dynamic community detection method which optimizes a novel community scoring metric, called permanence. DyPerm incrementally modifies the community structure by updating those communities where the editing of nodes and edges has been performed, keeping the rest of the network unchanged. We present strong theoretical guarantees to show how/why mere updates on the existing community structure leads to permanence maximization in dynamic networks, which in turn decreases the computational complexity drastically. Experiments on both synthetic and six real-world networks with given ground-truth community structure show that DyPerm achieves (on average) 35% gain in accuracy (based on NMI) compared to the best method among four baseline methods. DyPerm also turns out to be 15 times faster than its static counterpart.