Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 51, Issue 9, Pages 5274-5288Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2947516
Keywords
Prediction algorithms; Indexes; Network topology; Perturbation methods; Ant colony optimization; Social networking (online); Topology; Ant colony optimization; chaotic perturbation; complex networks; link prediction
Funding
- National Natural Science Foundation of China [61503285, 61772367, U1636205]
- Municipal Natural Science Foundation of Shanghai [17ZR1446000]
- Program of Science and Technology Innovation Action of Science and Technology Commission of Shanghai Municipality [17511105204]
- Shanghai Municipal Commission of Economy and Informatization [18XI-05]
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The proposed chaotic ant colony optimized (CACO) link prediction algorithm shows significantly higher prediction accuracy and robustness in experiments, outperforming most state-of-the-art algorithms.
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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