一种基于网络拓扑的关键节点挖掘算法

A topology-based heuristics algorithm for critical node detection

关键节点挖掘在研究网络结构和性能方面具有广泛且重要的应用潜力.提出了一种基于网络拓扑的关键节点挖掘算法,以解决关键节点问题.该算法挖掘网络社区结构,使用基于拓扑的中心性指标选择节点集,通过迭代地交换该节点集和原网络中的部分点来优化网络连通性指标.同时,采用改进的局部搜索和加权随机选择机制,以加强搜索能力和克服局部最优陷阱.此外,为了改进初始解的质量,提出了一种基于网络拓扑的节点中心性指标.在多个合成和真实的网络数据集上与一些先进算法的比较试验表明,本研究提出的算法具有更好的精度和鲁棒性.

The mining of critical nodes has a wide and important potential application in studying network structures and performances.A topology-based critical node mining algorithm is proposed to solve the critical node problem.The algorithm mines the community structure of the network and selects a set of nodes based on topological centrality indicators.Then,this node set and part of the original network are iteratively exchanged to optimize the network connectivity index.At the same time,an improved local search and weighted random selection mechanism are adopted to strengthen the search ability and overcome local optimal traps.In addition,a node centrality indicator based on network topology is proposed to improve the quality of the initial solution.Experiments on multiple synthetic and real network datasets between the proposed algorithm and other advanced algorithms show that the proposed algorithm has better accuracy and robustness.