论文标题
使用动态Markov过程对节点的扩散影响进行排名
Rank the spreading influence of nodes using dynamic Markov process
论文作者
论文摘要
在实践和研究中对节点的扩散影响进行排名至关重要。排名节点扩散能力的关键是评估爆发期间目标节点感染易感节点的比例。在本文中，我们通过整合马尔可夫链和扩展过程来评估初始散布器的暴发大小，提出动态马尔可夫过程（DMP）方法。遵循马尔可夫过程的想法，该方法通过调整状态过渡矩阵并评估其受感染的邻居感染的易感节点的概率来解决非线性耦合的问题。我们已经采用了易感感染的反射（SIR）和易感感染感染的模型（SIS）模型，以在现实世界中的静态和时间网络上测试此方法。我们的结果表明，DMP方法可以比以前的单个和多驱动器更准确地评估节点的爆发量。此外，在扩散过程中，它也可以用来准确对节点的影响进行排名。
Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node's spreading ability is to evaluate the fraction of susceptible nodes been infected by the target node during the outbreak, i.e., the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbours. We have employed the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes' outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.
