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

2026-01-22

AI Summary

This study addresses the inherent bias in existing statistical inference methods for community detection in temporal networks, which tend to under- or over-represent communities of extremely small or large sizes. The authors model the temporal network as a multilayer network and demonstrate that generative models based on uniform distributions or discrete-time Markov processes introduce systematic biases in community assignment. To mitigate this issue, they propose a novel generative model that leverages the complete community structure from the previous time layer to inform the generation of the current layer’s topology. This approach effectively reduces the bias toward extreme community sizes. Both theoretical analysis and empirical experiments confirm that the proposed method significantly improves the accuracy of inferred community structures. The implementation code is publicly available.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Temporal Planning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• temporal networks
• community detection
• statistical inference
• community-size bias
• multilayer networks

Innovation

• temporal networks
• community detection
• statistical inference
• community-size bias
• generative model