In large-scale complex network spaces,quickly identifying structural hole nodes is of great significance for controlling the spread of viruses and public opinion.Aiming at the problem that the existing methods for identifying structural hole nodes have low recognition accuracy when the network structure changes,this paper proposed a structural hole node recognition algorithm.The algorithm combined adjacency information entropy and adjacency centrality based on multi-dimensional attribute mapping and fusion.The algorithm used weighted adjacency information entropy as the amount of information of neighbor nodes,used adjacency centrality to measure the importance of a node in propagating information about its neighbor nodes,and identified key structural hole nodes in the network by representing the local attributes of structural hole nodes as the ability of nodes to propagate information.Experimental results show that,compared with existing methods,under datasets with different network scales and network structures,the total scores of the three evaluation indicators of ξ,τ and network average information entropy are 0.470,1.679,and 4.027,respectively,which are all optimal.It shows that the algorithm has more superior and stable performance.Moreover,the algorithm still has a low time cost when applied to large-scale networks.
structural holemulti-dimensional attribute fusioninformation dissemination capabilitiesadjacency information entropyadjacency centrality