Improving Community Detection in Academic Networks by Handling Publication Bias

TL;DR

This paper presents a novel method for community detection in academic networks that uses publication content and a cloning strategy to better capture interdisciplinary research interests, addressing publication bias.

Contribution

It introduces a cloning approach that clusters publications to improve community detection and uncover interdisciplinary research connections.

Findings

Cloning improves community detection accuracy.

Broader research interests are uncovered.

More meaningful collaboration opportunities are identified.

Abstract

Finding potential research collaborators is a challenging task, especially in today's fast-growing and interdisciplinary research landscape. While traditional methods often rely on observable relationships such as co-authorships and citations to construct the research network, in this work, we focus solely on publication content to build a topic-based research network using BERTopic with a fine-tuned SciBERT model that connects and recommends researchers across disciplines based on shared topical interests. A major challenge we address is publication imbalance, where some researchers publish much more than others, often across several topics. Without careful handling, their less frequent interests are hidden under dominant topics, limiting the network's ability to detect their full research scope. To tackle this, we introduce a cloning strategy that clusters a researcher's publications and treats each cluster as a separate node. This allows researchers to be part of multiple communities, improving the detection of interdisciplinary links. Evaluation on the proposed method shows that the cloned network structure leads to more meaningful communities and uncovers a broader set of collaboration opportunities.