Affiliations: [a] School of Information Technology, Deakin University, Australia. E-mails: kmajbour@deakin.edu.au, jingyu.hou@deakin.edu.au | [b] Department of Computing, Kharazmi University, Iran. E-mail: majbourii.adel@gmail.com | [c] Faculty of Computer & Electrical Engineering, Qazvin Islamic Azad University, Iran. E-mail: s.khodayi20@gmail.com | [d] School of Software, University of Technology Sydney, Australia. E-mail: wanlei.zhou@uts.edu.au | [e] Faculty of Engineering, Khavaran Higher Education Institute, Iran. E-mail: s.saedy@hotmail.com | [f] Faculty of Computer Engineering, University of Kurdistan, Sanandaj, Iran. E-mail: me.rostami@gmail.com
Abstract: One of the most important challenges of social networks is to predict information diffusion paths. Studying and modeling the propagation routes is important in optimizing social network-based platforms. In this paper, a new method is proposed to increase the prediction accuracy of diffusion paths using the integration of the ant colony and densest subgraph algorithms. The proposed method consists of 3 steps; clustering nodes, creating propagation paths based on ant colony algorithm and predicting information diffusion on the created paths. The densest subgraph algorithm creates a subset of maximum independent nodes as clusters from the input graph. It also determines the centers of clusters. When clusters are identified, the final information diffusion paths are predicted using the ant colony algorithm in the network. After the implementation of the proposed method, 4 real social network datasets were used to evaluate the performance. The evaluation results of all methods showed a better outcome for our method.
Keywords: Diffusion paths prediction, information diffusion patterns, densest subgraphs, ant colony algorithm, centrality
