Identifying accurate link predictors based on assortativity of complex networks.

ABSTRACT: Link prediction algorithms in complex networks, such as social networks, biological networks, drug-drug interactions, communication networks, and so on, assign scores to predict potential links between two nodes. Link prediction (LP) enables researchers to learn unknown, new as well as future interactions among the entities being modeled in the complex networks. In addition to measures like degree distribution, clustering coefficient, centrality, etc., another metric to characterize structural properties is network assortativity which measures the tendency of nodes to connect with similar nodes. In this paper, we explore metrics that effectively predict the links based on the assortativity profiles of the complex networks. To this end, we first propose an approach that generates networks o

SUBMITTER: Al Musawi AF 

PROVIDER: S-EPMC9613685 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature