Node importance ranking method in complex network based on gravity method

Abstract

How to use quantitative analysis methods to identify which nodes are the most important in complex network, or to evaluate the importance of a node relative to one or more other nodes, is one of the hot issues in network science research. At present, a variety of effective models have been proposed to identify important nodes in complex network. Among them, the gravity model regards the coreness of nodes as the mass of nodes, the shortest distance between nodes as the distance between objects, and comprehensively considers the local information of nodes and path information to identify influential nodes. However, only the coreness is use to represented as the quality of the object, and the factors considered are relatively simple. At the same time, some studies have shown that the network can easily identify the core-like group nodes with local high clustering characteristics as core nodes when performing k-core decomposition,which leads to the inaccuracy of the gravity algorithm. Based on the universal gravitation method, considering the node h index, the number of node cores and the location of node structural holes, this paper proposes an improved algorithm ISM and its extended algorithm ISM<sub>+</sub>. The SIR model is used to simulate the propagation process on several classical real networks and artificial networks, and the results show that the proposed algorithm can better identify important nodes in the network than other centrality indicators.

Node importance measurement based on the improved K-shell in complex network

“…The classical node importance sorting methods include Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality(CC),K-shell decomposition, etc.Ruan Yirun [4] and others proposed a node importance scoring algorithm based on the node H index, node Ks value and the location of node structural holes.Xiong Caiquan [5] and others proposes based on the Ks position and neighbor within two steps to identify influential nodes in complex networks. Considering the local topology and global position of nodes, the method is more accurate.Hu Gang [6] and others proposed an approach based on the relationship between nodes and their direct and indirect neighbor nodes.this method extract the relationships,and calculates the information-entropy of node.You Qianjing [7] and others proposed a node importance evaluation method based on overlapping box covering algorithm.this method fused the local and global features of complex networks.…”

Dynamic Identification of Important Nodes in Complex Networks based on the KPDN-INCC Method

“…Shao's team [13] used local neighborhood priority asynchronous H computation to accelerate convergence speed. Ruan's team [14] constructed a two-step neighborhood model to evaluate node importance, which can be applied to large-scale dynamic networks.…”

Dynamic identification of important nodes in complex networks based on the KPDN–INCC method

“…Shao's team 13 used local neighborhood priority asynchronous H computation to accelerate convergence speed. Ruan's team 14 constructed a two-step neighborhood model to evaluate node importance, which can be applied to largescale dynamic networks.…”