A novel semi local measure of identifying influential nodes in complex networks

Wang, Xiaojie; Slamu, Wushour; Guo, Wenqiang; Wang, Sixiu; Ren, Yan

doi:10.1016/j.chaos.2022.112037

Cited by 14 publications

(4 citation statements)

References 34 publications

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“…Could it be that the semi-SAVC adoption of a high-order line graph resulted in an increase in identification accuracy despite halving its order? This led us to the association with the existence of the saturation effect of network information [ 48 , 49 ]. To investigate this, based on the nonlinear propagation model, the influence variations of the top 1% ranked nodes in multiple hypergraph datasets were explored with changes in the order of the high-order line graph corresponding to the hypergraph.…”

Section: Methods Evaluationmentioning

confidence: 99%

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy

Hu,

Tian,

Zhang

2023

Entropy

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Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph’s s-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation.

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Section: Methods Evaluationmentioning

confidence: 99%

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy

Hu,

Tian,

Zhang

2023

Entropy

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“…The degree density from a node to the target node is considered as the contact area A , the higher the degree density of the acceptance node, the higher the influence, and the higher the output value. The distance between two nodes is used to calculate ΔL , the greater the distance, the smaller the output value 36 . With the help of a heat conduction model formula, the value of Q is calculated as the output value for the target node.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Excavating important nodes in complex networks based on the heat conduction model

Hu,

Zheng,

et al. 2024

Sci Rep

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Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

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“…In particular, k i denotes the degree of node i in equation (5). Bc i and Bc max are the betweenness of node i and the maximum betweenness among all nodes in equation ( 6), respectively.…”

Section: J Stat Mech (2023) 083402mentioning

confidence: 99%

“…Complex networks are abstract models for understanding complex systems in the real world [1], which can effectively explain the relationships between objects in complex systems, and their breadths involve many fields such as computer science [2], biological science [3] and social science [4], etc. In these research fields, a significant topic is to capture critical nodes in the network [5]. Compared with other nodes in the network, critical nodes exert a greater impact on the whole network.…”

Section: Introductionmentioning

confidence: 99%

Identifying influential nodes in complex networks based on network embedding and local structure entropy

Lu,

Yang,

Zhang

2023

J. Stat. Mech.

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The identification of influential nodes in complex networks remains a crucial research direction, as it paves the way for analyzing and controlling information diffusion. The currently presented network embedding algorithms are capable of representing high-dimensional and sparse networks with low-dimensional and dense vector spaces, which not only keeps the network structure but also has high accuracy. In this work, a novel centrality approach based on network embedding and local structure entropy, called the ELSEC, is proposed for capturing richer information to evaluate the importance of nodes from the view of local and global perspectives. In short, firstly, the local structure entropy is used to measure the self importance of nodes. Secondly, the network is mapped to a vector space to calculate the Manhattan distance between nodes by using the Node2vec network embedding algorithm, and the global importance of nodes is defined by combining the correlation coefficients. To reveal the effectiveness of the ELSEC, we select three types of algorithms for identifying key nodes as contrast approaches, including methods based on node centrality, optimal decycling based algorithms and graph partition based methods, and conduct experiments on ten real networks for correlation, ranking monotonicity, accuracy of high ranking nodes and the size of the giant connected component. Experimental results show that the ELSEC algorithm has excellent ability to identify influential nodes.

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A novel semi local measure of identifying influential nodes in complex networks

Cited by 14 publications

References 34 publications

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy

Excavating important nodes in complex networks based on the heat conduction model

Identifying influential nodes in complex networks based on network embedding and local structure entropy

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