利用网络表征学习辨识复杂网络节点影响力

发现复杂网络中最具影响力的节点,有助于分析和控制网络中的信息传播,具有重要的理论意义和实用价值.传统的确定节点影响力的方法大多基于网络的邻接矩阵、拓扑结构等,普遍存在数据维度高和数据稀疏的问题,基于网络表征学习,本文提出了一种局部中心性指标来辨识网络中高影响节点(NLC),首先采用DeepWalk算法,把高维网络中的节点映射为一个低维空间的向量表示,并计算局部节点对之间的欧氏距离;接着根据网络的拓扑结构,计算每个节点在信息的传播过程中,对所在局部的影响力大小,用以识别高影响力节点.在八个真实网络中,以SIR和SI传播模型作为评价手段,将NLC算法和度中心性、接近中心性、介数中心性、邻居核中心性、半局部中心性做了对比,结果表明NLC算法具有良好的识别高影响力传播节点的性能.

Identification of Node Influence Using Network Representation Learning in Complex Network

Finding the most influential propagation nodes in complex networks is helpful to analyze and control the propagation of information in the network,which is of great theoretical significance and practical value.Most of the traditional methods for determining the influence of nodes are based on the adjacency matrix and topology of the network,and the problems of high data dimension and data sparsity are common.Based on Network Representation Learning,this paper proposes an algorithm to identify the high influence propagation nodes of the network(NLC).Firstly,the deepwalk algorithm is used to map the nodes in a high-dimensional network into a vector representation of a low-dimensional space and calculate the euclidean distance between local node pairs.Then,according to the topology of the network,the influence of each node on the local area during the propagation of information is calculated to identify the high-influence nodes.In eight real networks,SIR and SI propagation models are used as evaluation methods,comparing the NLC algorithm with degree centrality,closeness centrality,betweeness centrality,neighborhood coreness,and semi-local centrality,the results show that NLC algorithm has good performance in identifying high-influence propagation nodes.