Enhancing Scientific Collaborations using Community Detection and Document Clustering

Community detection is the process of extracting community structured subgraphs from community networks. Most research regarding community detection has focused on the network structure without taking the content associated with the nodes into account. In this paper, we propose a new method for enhancing a co-authorship network's structure using clustering. Specifically, considering the clustering process, we use a sequence with proved performance between the WordNet lemmatizer, Document Embeddings and Spherical K-Means, while choosing the Louvain algorithm for community detection. Thus, we improve the Louvain's community detection algorithm modularity by interconnecting the author nodes for the articles clustered together. To evaluate our method, we collected a dataset containing articles' abstracts and authors. The experimental results show that our method suggests potential new collaborations by adding vertices to the graph after analysing the textual content.