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Competitive influence maximization has been studied for several years, andvarious frameworks have been proposed to model different aspects of informationdiffusion under the competitive environment. This work presents a new gameboardfor two competing parties with some new features representing loyalty in socialnetworks and reflecting the attitude of not completely being loyal to a partywhen the opponent offers better suggestions. This behavior can be observed inmost political occasions where each party tries to attract people by makingbetter suggestions than the opponent and even seeks to impress the fans of theopposition party to change their minds. In order to identify the best move ineach step of the game framework, an improved Monte Carlo tree search isdeveloped, which uses some predefined heuristics to apply them on thesimulation step of the algorithm and takes advantage of them to search amongchild nodes of the current state and pick the best one using an epsilon-greedyway instead of choosing them at random. Experimental results on synthetic andreal datasets indicate the outperforming of the proposed strategy against somewell-known and benchmark strategies like general MCTS, minimax algorithm withalpha-beta pruning, random nodes, nodes with maximum threshold and nodes withminimum threshold.

LBCIM: Loyalty Based Competitive Influence Maximization with epsilon-greedy MCTS strategy