An Ant-Colony-Optimization-Based Method for Community-Aware Link Prediction in Social Networks

Link prediction is one of the primary issues in the social network analysis domain, focusing on predicting the emergence of future links. Among state-of-the-art approaches, metaheuristic-based methods stand out, as they can combine existing heuristic measures for link formation with greedy exploration, thereby improving the effectiveness of future link prediction. Many of these heuristics are designed based on the structural characteristics of social networks. However, only a limited number of methods have incorporated community structure, a fundamental property of social networks. In this article, a novel link prediction method is introduced by integrating the concept of community structure into the ant colony optimization algorithm. Specifically, three common neighbor-based heuristics are extended to consider the community structure and the distinct nature of intra-community and inter-community relationships. These enhanced heuristics are then utilized in the ant colony optimization search mechanism. The effectiveness of the proposed method is evaluated against several benchmark techniques using nine real-world datasets. The results demonstrate the outperformance of the presented approach considering AUC and precision comparison metrics.

کلیدواژه ها:

Link Prediction ، Ant colony optimization ، Community structure ، Nodes similarity