LINK PREDICTION BASED ON TOPOLOGICAL AND CONTENT ANALYSIS IN CO-AUTHORSHIP NETWORKS

In network analysis, the prediction of the connections or associations between entities or nodes within the network becomes important. Link Prediction is the problem of predicting or identifying the existence of a link between two entities in a network. However, it still the main issue in the complex network data application field, particularly in the type of analysis related to co-authorship networks despite its wide usage. Topological methods and content-based methods are the two different approaches that have been proposed for the link prediction in collaboration networks. However, topological methods are based on the structural analysis of the network, and content-based approaches rely on textual information from academic papers in the network. In this paper, we introduce the Content and Graph-Based Link Prediction (CGLP) approach, which integrates topological and content-based features from networks in a hybrid manner for predicting links in co-authorship networks. The efficacy of the proposed approach was already tested using three academic datasets: Hep-th, Hep-lat, and AMC by applying various machine learning models. Results indicated that all models showed almost the same efficiency on all three datasets and outperformed the state-of-the-art approach with a maximum F1 score of 98.05% and ROC AUC of 98.74%.