# A Fast and Efficient Incremental Approach toward Dynamic Community Detection

## TL;DR

This paper introduces a fast incremental method called Δ-screening for dynamic community detection, enabling efficient updates in evolving networks while maintaining high quality, demonstrated by significant speedups on large real-world datasets.

## Contribution

The paper presents a generic Δ-screening technique that accelerates dynamic community detection by selectively reevaluating affected vertices, adaptable to existing modularity-based methods.

## Key findings

- Achieved a 3x speedup on large real-world networks.
- Maintained output quality despite heuristic approach.
- Enabled identification of optimal temporal resolution intervals.

## Abstract

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network.