Finding potential research collaborators is a challenging task, especially in today’s fast-growing, interdisciplinary research landscape. While traditional methods rely on observable ties like co-authorships and citations, 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 key 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 capture 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 belong to multiple communities, improving the detection of interdisciplinary links. Evaluation shows that the cloned network leads to more meaningful communities and uncovers broader collaboration opportunities.
Handling Publication Imbalance for Effective Community Detection in Scholarly Networks