Integrating ant colony algorithm and node centrality to improve prediction of information diffusion in social networks

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11342 LNCS pp. 381 - 391
Issue Date:
Filename Description Size
477537_Print.indd_paper.pdfPublished version486.38 kB
Adobe PDF
Full metadata record
© Springer Nature Switzerland AG 2018. One 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.
Please use this identifier to cite or link to this item: