用内容识别学者兴趣，克隆论文节点提升跨学科合作发现
Improving Community Detection in Academic Networks by Handling Publication Bias
- 基于SciBERT的BERTopic提取论文主题，构建跨领域研究网络
- 通过克隆策略让高产学者在多个主题中独立成点，避免被主流话题掩盖
- 适合需要挖掘潜在跨学科合作者的研究人员和学术平台
在快速发展的跨学科研究环境中，寻找潜在合作者是一项挑战。传统方法依赖共著和引用关系构建研究网络，而本文仅利用论文内容，通过微调的SciBERT与BERTopic构建基于主题的研究网络，实现跨学科研究者匹配。主要挑战在于发表数量不均：部分学者发表量大且涉猎多领域，其非主流兴趣常被主导主题掩盖。为此，我们提出克隆策略，将每位学者的论文按主题聚类，每个簇作为独立节点，使学者可参与多个社区。评估表明，该方法生成的网络能形成更合理的社区结构，揭示更广泛的协作机会。
原文摘要 · Abstract (English)
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.
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