Influential node identification by aggregating local structure information

Wang, Feifei; Sun, Zejun; Gan, Quan; Fan, Aiwan; Shi, Hongyin; Hu, Haifeng

doi:10.1016/j.physa.2022.126885

Cited by 19 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We selected ten different propagation rates 𝛽, where 𝛽 ∈ 𝛽 0.05, 𝛽 0.05 [39] and set 𝛾 𝛽/1.2. For these networks, we simulated each node independently 100 times and repeated the calculation 10 times, totally 1000 simulations [40]. Finally, the average value of all simulations is taken as the real propagation influence of a node.…”

Section: Results Of Accuracymentioning

confidence: 99%

Ranking influential nodes by combining normalized degree centrality and fine-grained K-Shell

Mao

Yang²,

Fan³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

View full text Add to dashboard Cite

Identifying influential nodes is one of the crucial issues for controlling the network propagation process and exploring network properties in complex networks. Nevertheless, the accuracy of existing methods is still a challenge. In this paper, we rank influential nodes by considering tow aspects. On one hand, a normalized degree centrality is proposed to measure the local influence of each node. On the other hand, an improved fine-grained K-Shell decomposition is defined to measure the spreading ability of neighbors of a node. Further, a novel ranking measure is proposed by combining the normalized degree centrality and fine-grained K-Shell (NDF-FKS). The Susceptible-Infected-Recovery (SIR) model is used to simulate the network propagation process. Experiments with the model are performed on eight synthetic networks and four real networks. The NDF-FKS compared with six measures for accuracy and resolution. The results show that the accuracy of NDF-FKS outperforms existing six measures and has a competitive performance on distinguishing influential nodes.

show abstract

Section: Results Of Accuracymentioning

confidence: 99%

Ranking influential nodes by combining normalized degree centrality and fine-grained K-Shell

Mao

Yang²,

Fan³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of ELSEC method, we describe in detail the evaluation metrics and the spreading model, and compare the presented method with two classical algorithms and five state-of-the-art approaches on nine datasets, including BC [11], kshell (KS) [19], importance of global and local structure (GLS) [43], local and global centrality (LGC) [15], where the parameter α = 1, generalized gravity centrality of nodes (GGC) [44], where the parameter α = 2, effective gravity model (EGM) [45] and centrality of aggregated local structure information (ALSI) [46]. In addition, we also compare the ELSEC method with optimal decycling based algorithm and graph partition based method, including MinSum [47] and generalized network-dismantling (GND) [48].…”

Section: Experimental Preparationmentioning

confidence: 99%

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

Lu,

Yang,

Zhang

2023

J. Stat. Mech.

View full text Add to dashboard Cite

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.

show abstract

“…In the real world, the phenomenon of networks has a very broad application, and complex systems with numerous entities can be represented as networks 1 . A complex network can be thought of as the abstract representation of a complex system 2 , where the nodes represent the entities in the system and the edges represent the relationships between them.…”

Section: Introductionmentioning

confidence: 99%

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

Hu,

Zheng,

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Influential node identification by aggregating local structure information

Cited by 19 publications

References 33 publications

Ranking influential nodes by combining normalized degree centrality and fine-grained K-Shell

Ranking influential nodes by combining normalized degree centrality and fine-grained K-Shell

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

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

Contact Info

Product

Resources

About