An incremental local-first community detection method for dynamic graphs

TL;DR

This paper introduces an incremental community detection algorithm for dynamic large-scale graphs, enabling real-time analysis of network growth with significantly improved speed and maintained accuracy.

Contribution

It presents a novel incremental extension of the DEMON algorithm that efficiently detects communities in evolving networks, outperforming batch methods in speed while preserving accuracy.

Findings

Achieves up to 107 times faster community detection than DEMON

Capable of processing networks with over 400,000 vertices in less than a second

Maintains comparable accuracy to batch algorithms in dynamic graph analysis

Abstract

Community detections for large-scale real world networks have been more popular in social analytics. In particular, dynamically growing network analyses become important to find long-term trends and detect anomalies. In order to analyze such networks, we need to obtain many snapshots and apply same analytic methods to them. However, it is inefficient to extract communities from these whole newly generated networks with little differences every time, and then it is impossible to follow the network growths in the real time. We proposed an incremental community detection algorithm for high-volume graph streams. It is based on the top of a well-known batch-oriented algorithm named DEMON[1]. We also evaluated performance and precisions of our proposed incremental algorithm with real-world big networks with up to 410,236 vertices and 2,439,437 edges and computed in less than one second to detect communities in an incremental fashion - which achieves up to 107 times faster than the original algorithm without sacrificing accuracies.