在谣言传播过程中,针对度不同的节点具有的辨识能力不同,结合节点度定义一种新的博弈收益,借助博弈论建立一种动态复杂网络演化模型.该模型考虑到谣言传播往往与节点利益相关这一特点,通过引入辨识能力描述不同节点的非一致传播率,研究谣言在该模型上的传播动力学行为,并提出两种谣言抑制策略.随后,利用两种典型网络模型进行仿真实验,并在Facebook真实网络数据中对仿真结果进行验证.研究表明,谣言模糊程度对BA(Barabási-Albert)无标度网络和Facebook网络中谣言传播速率及达到稳定状态所需时间影响较小,随着谣言模糊程度增大,谣言在网络中传播范围变大,相对于WS(Watts-Strogtz)小世界网络,谣言更容易在BA无标度网络和Facebook网络中传播;研究还发现,免疫收益增加值相同时,与BA无标度网络和Facebook网络相比,WS小世界网络中免疫节点的增长幅度更大;此外,通过节点危害程度进行抑制比通过博弈收益进行抑制具有更好的谣言抑制效果.%In the process of rumor spreading,nodes with different degrees of recognition have different recognition abilities.A evolution model of dynamic complex network was proposed based on game theory,in which a new game gain was defined according to node degree.In this model,considering the fact that rumor propagation was often related to node interests,the non-uniform propagation rates of different nodes and propagation dynamics of rumors were described by introducing the recognition ability,and two rumor suppression strategies were proposed.The simulation were conducted on two typical network models and verified in the Facebook real network data.The research demonstrates that the fuzzy degree of rumor has little effect on the rmnor propagation rate and the time required to reach steady state in BA scale-free network and Facebook network.As rumors are increasingly fuzzy,the scope of rumor in the network is expanding.Compared with WattsStrogtz (WS) small-world network,rumors spread more easily in BA scale-free network and Facebook network.The study also finds out that immune nodes in the WS small-world network grow more rapidly than immune nodes in BA scale-free network and Facebook network with the same added value of immune benefits.In addition,there is a better rumor suppression effect by suppressing the degree of node hazard than by suppressing the game gain.
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