A Chaotic Ant Colony Optimized Link Prediction Algorithm.
IEEE transactions on systems, man, and cybernetics Systems(2021)
摘要
The mining missing links and predicting upcoming links are two important topics in the link prediction. In the past decades, a variety of algorithms have been developed, the majority of which apply similarity measures to estimate the bonding probability between nodes. And for these algorithms, it is still difficult to achieve a satisfactory tradeoff among precision, computational complexity, robustness to network types, and scalability to network size. In this article, we propose a chaotic ant colony optimized (CACO) link prediction algorithm, which integrates the chaotic perturbation model and ant colony optimization. The extensive experiments on a wide variety of unweighted and weighted networks show that the proposed algorithm CACO achieves significantly higher prediction accuracy and robustness than most of the state-of-the-art algorithms. The results demonstrate that the chaotic ant colony effectively takes advantage of the fact that most real networks possess the transmission capacity and provides a new perspective for future link prediction research.
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关键词
Prediction algorithms,Indexes,Network topology,Perturbation methods,Ant colony optimization,Social networking (online),Topology,Ant colony optimization,chaotic perturbation,complex networks,link prediction
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