Geometric Deep Learning Strategies for the Characterization of Academic Collaboration Networks

Abstract

This paper examines how geometric deep learning techniques may be employed to analyze academic collaboration networks (ACNs) and how using textual information drawn from publications improves the overall performance of the system. The proposed experimental pipeline was used to analyze the collaboration network of the Machine Learning Genoa Center (MaLGa) research group. First, we find the optimal method for embedding the input data graph and extracting meaningful keywords for the available publications. We then use Graph Neural Networks (GNN) for node type and research topic classification. Finally, we explore how the resulting corpus can be used to create a recommender system for optimal navigation of the ACN. Our results show that the GNN-based recommender system achieved high accuracy in suggesting unexplored nodes to users. Overall, this study demonstrates the potential for using geometric deep learning and Natural Language Processing (NLP) to best represent the scientific production of ACNs. In the future, we plan to incorporate the temporal nature of the data and navigation statistics of users exploring the graph as additional input for the recommender system.