Influence Role Recognition and LLM-Based Scholar Recommendation in Academic Social Networks

Identifying scholars and their relevant publications in interdisciplinary collaborations within an academic social network (ASN) can help drive new scientific knowledge discovery. This involves a challenging and time-consuming process, which requires scholar's influence role recognition in a scholar team for a given research task. In this paper, we propose a novel “ScholarInfluencer” recommendation system that: (a) uses a classification model combined with network analysis on a heterogeneous knowledge graph to recognize the scholar influencers within interdisciplinary teams of collaborators, and (b) features a large language model (LLM) to use influence role recognition results to support user queries to produce pertinent scholar and their publication recommendations. Our novel approach involves building a heterogeneous knowledge graph using diverse ASN datasets involving entities such as scholars, publications, research grants, and the relationship among these entities. We perform an evaluation of ScholarInfluencer using four widely-used ASN datasets (i.e., NSF, DBLP, Cora and CA-HepTh). Our experiment results show that our influence role recognition model outperforms the state-of-the-art models across the different datasets; especially in the case of the NSF dataset, our model outperforms by up to 13.6%. Further, we show how our recommendation model with role recognition outperforms the model without role recognition across the different datasets; especially in the case of the NSF dataset, our model outperforms by 7%.