Competitive influence maximization has been studied for several years, and various frameworks have been proposed to model different aspects of information diffusion under the competitive environment. This work presents a new gameboard for two competing parties with some new features representing loyalty in social networks and reflecting the attitude of not completely being loyal to a party when the opponent offers better suggestions. This behavior can be observed in most political occasions where each party tries to attract people by making better suggestions than the opponent and even seeks to impress the fans of the opposition party to change their minds. In order to identify the best move in each step of the game framework, an improved Monte Carlo tree search is developed, which uses some predefined heuristics to apply them on the simulation step of the algorithm and takes advantage of them to search among child nodes of the current state and pick the best one using an epsilon-greedy way instead of choosing them at random. Experimental results on synthetic and real datasets indicate the outperforming of the proposed strategy against some well-known and benchmark strategies like general MCTS, minimax algorithm with alpha-beta pruning, random nodes, nodes with maximum threshold and nodes with minimum threshold.

翻译：竞争影响力最大化已被研究多年，各种框架被提出以模拟竞争环境下信息扩散的不同方面。本文提出了一个用于两个竞争方的新博弈棋盘，具备若干新特征，这些特征代表了社交网络中的忠诚度，并反映了当对手提出更好建议时，个体不会完全忠于某一方的态度。这种行为在大多数政治场合中可见，其中每一方都试图通过提出比对手更好的建议来吸引人群，甚至试图打动反对阵营的粉丝以改变其立场。为了在博弈框架的每一步中识别最佳行动，我们开发了一种改进的蒙特卡洛树搜索算法，该算法使用若干预定义启发式策略，将其应用于算法的模拟步骤，并利用这些策略在当前状态的子节点中进行搜索，通过ε-贪心方式而非随机选择来选取最优节点。在合成数据集和真实数据集上的实验结果表明，该策略优于一些知名基准策略，如通用MCTS、带Alpha-Beta剪枝的极小极大算法、随机节点、最大阈值节点和最小阈值节点。