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Integrating ant colony algorithm and node centrality to improve prediction of information diffusion in social networks
conference contribution
posted on 2018-01-01, 00:00 authored by Adel Yazdi, Saeid Khodayi, Jingyu HouJingyu Hou, Wanlei Zhou, Saeed Saedy, Kasra Majbouri YazdiOne of the latest and most important research topics in the field of information diffusion, which has attracted many social network analyst experts in recent years, is how information is disseminated on social networks. In this paper, a new method is proposed by integration of ant colony algorithm and node centrality to increase the prediction accuracy of information diffusion paths on social networks. In the first stage of our approach, centrality of all nodes in the network is calculated. Then, based on the distances of nodes in the network and also ant colony algorithm, the optimal path of propagation is detected. After implementation of the proposed method, 4 real social network data sets were used to evaluate its performance. The evaluation results of all methods showed a better outcome for our method.
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Security, Privacy, and Anonymity in Computation, Communication, and Storage. International Conference (11th: 2018 : Melbourne, Victoria)Volume
11342Series
Lecture Notes in Computer SciencePagination
381 - 391Publisher
SpringerLocation
Melbourne, VictoriaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-12-11End date
2018-12-13ISSN
0302-9743eISSN
1611-3349ISBN-13
978-3-030-05344-4Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, Springer Nature Switzerland AGEditor/Contributor(s)
Guojun Wang, Jinjun Chen, Laurence YangTitle of proceedings
SpaCCS 2018: Proceedings of the 11th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and StorageUsage metrics
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