Community-Size Biases in Statistical Inference of Communities in Temporal Networks

TL;DR

This paper investigates biases in statistical inference methods for detecting communities in temporal networks, introduces a new model to reduce size bias, and demonstrates improved community detection accuracy especially for large or small communities.

Contribution

The paper identifies size biases in existing models and proposes a novel generative model that mitigates these biases in temporal community detection.

Findings

Existing models are biased against large or small communities.

The new model reduces size bias and improves detection accuracy.

Code is publicly available for replication.

Abstract

In the study of time-dependent (i.e., temporal) networks, researchers often examine the evolution of communities, which are sets of densely connected sets of nodes that are connected sparsely to other nodes. An increasingly prominent approach to studying community structure in temporal networks is statistical inference. In the present paper, we study the performance of a class of statistical-inference methods for community detection in temporal networks. We represent temporal networks as multilayer networks, with each layer encoding a time step, and we illustrate that statistical-inference models that generate community assignments via either a uniform distribution on community assignments or discrete-time Markov processes are biased against generating communities with large or small numbers of nodes. In particular, we demonstrate that statistical-inference methods that use such generative models tend to poorly identify community structure in networks with large or small communities. To rectify this issue, we introduce a novel statistical model that generates the community assignments of the nodes in given layer (i.e., at a given time) using all of the community assignments in the previous layer. We prove results that guarantee that our approach greatly mitigates the bias against large and small communities, so using our generative model is beneficial for studying community structure in networks with large or small communities. Our code is available at https://github.com/tfaust0196/TemporalCommunityComparison.