论文标题
重新采样社区检测以最大化复杂网络的传播
Resampling community detection to maximize propagation in complex network
论文作者
论文摘要
在理论和应用领域中识别复杂网络中重要的节点至关重要。少数此类节点具有确定信息传播的确定性能力，因此找到一组节点最大程度地提高网络中的传播是很重要的。基于基线排名方法，提出了各种改进的方法，但是不存在涵盖所有基本方法的一种增强方法。在本文中，我们提出了一种称为RCD-MAP的惩罚方法，该方法是在五种基线排名方法（学位中心性，紧密的中心性，中心性，K-Shell和Pagerank）与节点的本地社区信息的五种基线排名方法（学位中心，K-Shell和Pagerank之间）的缩写。我们通过重新采样来扰动原始图，以减少通过社区检测方法重叠和非重叠方法带来的偏见和随机性。为了评估我们识别方法的性能，SIR（易感感染的反射）模型用于模拟信息传播过程。结果表明，具有惩罚的方法在一般情况下，具有vaster繁殖范围的方法更好。
Identifying important nodes in complex networks is essential in theoretical and applied fields. A small number of such nodes have deterministic power to decide information spreading, so it is of importance to find a set of nodes that maximize the propagation in networks. Based on baseline ranking methods, various improved methods were proposed, but there does not exist one enhanced method that covers all the base methods. In this paper, we propose a penalized method called RCD-Map, which is short for resampling community detection to maximize propagation, on five baseline ranking methods(Degree centrality, Closeness centrality, Betweennees centrality, K-shell and PageRank) with nodes' local community information. We perturbed the original graph by resampling to decrease the biases and randomness brought by community detection methods-both overlapping and non-overlapping methods. To assess the performance of our identifying method, SIR(susceptible-infected-recovered) model is applied to simulate the information propagation process. The result shows that methods with penalties perform better with a vaster propagation range in general.
