Prediction optimization of diffusion paths in social networks using integration of ant colony and densest subgraph algorithms
Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy, Mehrdad Rostami
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 u sing 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.