ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities within Scholarly Social Networks

Detecting communities within dynamic academic social networks and connecting these community detection findings to search and retrieval interfaces presents a multifaceted challenge. We explore an information retrieval method that integrates both partitionbased and similarity-based network analysis to identify and recommend communities within content-based datasets. Our prototype "ScholarNodes" web interface bridges the gap between community detection algorithms (Louvain, K-means, Spectral clustering) and the BM25 (Best Matching 25) ranking algorithm within a cohesive user interface. From free-text keyword queries, ScholarNodes recommends collaborations, identifies local and external researcher networks, and visualizes an interdisciplinarity graph for individual researchers using the OpenAlex dataset, a global collection of academic papers and authors. Beyond the specific information retrieval use case, we discuss the broader applicability of the methods to generic social network analysis, community detection, and recommender systems. Additionally, we delve into the technical aspects of generating topical terms, community alignment techniques, and interface design considerations for integrating community detection algorithms into a search experience.