A novel metric for community detection

TL;DR

This paper introduces a new community detection metric based on link predictability within communities, addressing limitations of modularity and improving robustness and accuracy in identifying true communities.

Contribution

The paper proposes a novel community detection metric rooted in link predictability, offering enhanced robustness and the ability to identify false communities compared to modularity.

Findings

The new metric is more robust than traditional modularity.

It enables evaluation of algorithm stability across different networks.

It can detect false community assignments.

Abstract

Research into detection of dense communities has recently attracted increasing attention within network science, various metrics for detection of such communities have been proposed. The most popular metric -- Modularity -- is based on the so-called rule that the links within communities are denser than external links among communities, has become the default. However, this default metric suffers from ambiguity, and worse, all augmentations of modularity and based on a narrow intuition of what it means to form a "community". We argue that in specific, but quite common systems, links within a community are not necessarily more common than links between communities. Instead we propose that the defining characteristic of a community is that links are more predictable within a community rather than between communities. In this paper, based on the effect of communities on link prediction, we propose a novel metric for the community detection based directly on this feature. We find that our metric is more robustness than traditional modularity. Consequently, we can achieve an evaluation of algorithm stability for the same detection algorithm in different networks. Our metric also can directly uncover the false community detection, and infer more statistical characteristics for detection algorithms.