Multislice Modularity Optimization in Community Detection and Image Segmentation

TL;DR

This paper introduces a multislice modularity optimization method for community detection in networks and image segmentation, capable of identifying communities of varying sizes without prior knowledge, and demonstrates its effectiveness on social and image data.

Contribution

It presents a novel multislice modularity optimization approach that detects communities across multiple scales in networks and images without needing predefined community parameters.

Findings

Method performs well on social network data

Effective in image segmentation tasks

Does not require prior knowledge of community sizes

Abstract

Because networks can be used to represent many complex systems, they have attracted considerable attention in physics, computer science, sociology, and many other disciplines. One of the most important areas of network science is the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In this paper, we algorithmically detect communities in social networks and image data by optimizing multislice modularity. A key advantage of modularity optimization is that it does not require prior knowledge of the number or sizes of communities, and it is capable of finding network partitions that are composed of communities of different sizes. By optimizing multislice modularity and subsequently calculating diagnostics on the resulting network partitions, it is thereby possible to obtain information about network structure across multiple system scales. We illustrate this method on data from both social networks and images, and we find that optimization of multislice modularity performs well on these two tasks without the need for extensive problem-specific adaptation. However, improving the computational speed of this method remains a challenging open problem.