Influence Maximization Based Global Structural Properties: A Multi-Armed Bandit Approach

The influence maximization problem is defined by identifying the seed set that has the most influence on other users in the network, which when selected, the cascading process reaches a large number of users. We use a greedy algorithm and an epsilon-greedy algorithm from the MAB models in this work, unlike prior works that used the MAB models to quantify the unknown propagation probability in the diffusion models. In this paper, we did not also make any assumption regarding the diffusion models and tries to learn to identify the most influential users based on designed reward function "hybrid edge strength-similarity" using global centrality measures and by trying to find a tradeoff between exploitation and exploration strategies. The new proposed reward function initializes the MAB algorithms using global characteristics that quantify the strength of each arm (edge). The proposed reward will feed algorithms from MAB models uses hybridization of edge betweenness centrality and Jaccard similarity measures with some level of participation of each measure. Then, three algorithms are proposed for the extraction of relevant influencers, namely: SRI-CGSS FEXPL-GREEDY) algorithm which almost exploiting the best arm; the SRI_CGSS FEXPR-GREEDY which is almost exploring; and the SRI-MAB \epsilon -GREEDY algorithm that alternate between exploring and exploiting the best arms. We conduct extensive experiments on a large-scale graph in terms of influence spread, efficiency performance in terms of running time and space complexity, and how the reward parameters impact cumulative regret.