Improved similarity-based link prediction methods in trade networks by integrating community-based popularity

Link prediction is a vital aspect of analyzing network evolution and identifying potential connections in complex networks. Previous studies have primarily focused on the information of common neighbors between nodes, often overlooking the inherent attributes of nodes. This study proposes community-based popularity, an attribute of nodes that considers changes in the neighborhood over time in conjunction with the community structure. Based on this attribute, we improve similarity-based link prediction methods. The experiments utilized unweighted directed networks from three distinct types of trade to evaluate the effectiveness of the improved link prediction methods. The training and probe sets were divided in chronological order. The experimental results show that the improved methods provide better link prediction results than the compared methods.