Distance-Aware Influence Maximization Algorithm based on Random Walk
Abstract— In this paper, we investigate the distance-aware influence maximization problem on the
independent cascade(IC) model. A random walk based algorithm is presented to find the seed set to
maximizing the influence for a distance aware query. Random walk method is used to perform path sampling
to simulate the influence propagation process. Based on the result using random walk method, greedy method
is used to select the seed set. Our experimental results show that the algorithm can reasonably select the seed
set to maximize the influence propagation.
Index Terms— Influence maximization, Distance-aware, Random walk, Greedy method
Department of Computer Science, Yangzhou University, Yangzhou,CHINA
State Key Lab of Novel Software Tech, Nanjing University, Nanjing, CHINA
Cite: Yuwei Wang, Ling Chen, "Distance-Aware Influence Maximization Algorithm based on Random Walk," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 807-814, Hong Kong, 15-17 June, 2019.