A new evidential methodology of identifying influential nodes in complex networks

Bian, Tian; Deng, Yong

doi:10.1016/j.chaos.2017.05.040

Cited by 31 publications

(13 citation statements)

References 66 publications

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“…e D-S evidence theory-based method is among the alternative algorithmic approach to multisensor data fusion that tries to achieve refined estimates of "uncertainty" [37][38][39]. It employs a reliability function rather than probability to measure uncertainty, and it is widely used in the field of information and decision-making.…”

Section: D-s Evidence Eorymentioning

confidence: 99%

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Gao

Tao

et al. 2022

Complexity

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The structure formed by fossil energy trade among countries can be divided into multiple subcommodity networks. However, the difference of coupling mode and transmission mechanism between layers of the multirelationship network will affect the measurement of node importance. In this paper, a framework of multisource information fusion by considering data uncertainty and the classical network centrality measures is build. Then, the evidential centrality (EVC) indicator is proposed, by integrating Dempster–Shafer evidence theory and network theory, to empirically identify influential nodes of fossil energy trade along the Belt and Road Initiative. The initial result of the heterogeneity characteristics of the constructed network drives us to explore the core node issue further. The main detected evidential nodes include Russia, Kazakhstan, Czechia, Slovakia, Egypt, Romania, China, Saudi Arabia, and Singapore, which also have higher impact on network efficiency. In addition, cluster analysis discovered that resource endowment is an essential factor influencing country’s position, followed by geographical distance, economic level, and economic growth potential. Therefore, the above aspects should be considered when ensuring national trade security. At last, the rationality and comprehensiveness of EVC are verified by comparing with some benchmark centralities.

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Section: D-s Evidence Eorymentioning

confidence: 99%

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Gao

Tao

et al. 2022

Complexity

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“…Hongming Mo et al [38] proposed an evidential method for the influential node identification, which is based on the Dempster-Shafer evidence theory. Furthermore, Bian and Yong [39] introduced a new evidential centrality (NEC) algorithm, which is the extension of the evidential method. Tian et al [40] proposed an analytic hierarchy process (AHP), which works on the basis of multiple attribute decision-making (MADM) method; AHP is used to detect the important branch of every decision and choose the best nodes in the entire network.…”

Section: Related Workmentioning

confidence: 99%

Identification of Influential Nodes via Effective Distance‐based Centrality Mechanism in Complex Networks

et al. 2021

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Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.

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“…Its disadvantage is the lack of consideration of the global network structure and the influence of the surrounding nodes, therefore, in several cases, it is not sufficiently accurate. K-shell is a coarse-grained ranking method [34], in which a node is usually considered to have a higher influence if it is situated in the core position of the network even if its degree is small. The influence of large-degree nodes on the edge is often limited [35].…”

Section: Related Workmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Local Neighbor Contribution

et al. 2019

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The identification of influential nodes in complex networks has been widely used to suppress rumor dissemination and control the spread of epidemics and diseases. However, achieving high accuracy and comprehensiveness in node influence ranking is time-consuming, and there are issues in using different measures on the same subject. The identification of influential nodes is very important for the maintenance of the entire network because they determine the stability and integrity of the entire network, which has strong practical application value in real life. Accordingly, a method based on local neighbor contribution (LNC) is proposed. LNC combines the influence of the nodes themselves with the contribution of the nearest and the next nearest neighbor nodes, thus further quantifying node influence in complex networks. LNC is applicable to networks of various scales, and its time complexity is considerably low. We evaluate the performance of LNC through extensive simulation experiments on seven real-world networks and two synthetic networks. We employ the SIR model to examine the spreading efficiency of each node and compare LNC with degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, Hyperlink-Induced Topic Search(HITS), ProfitLeader, Gravity and Weighted Formal Concept Analysis(WFCA). It is demonstrated that LNC ranks nodes effectively and outperforms several state-of-theart algorithms. INDEX TERMS Complex networks, influential nodes, local structure, neighbor contribution.

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A new evidential methodology of identifying influential nodes in complex networks

Cited by 31 publications

References 66 publications

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Influential Nodes in the OBOR Fossil Energy Trade Network Based on D‐S Theory: Detection and Evolution Analysis

Identification of Influential Nodes via Effective Distance‐based Centrality Mechanism in Complex Networks

Identifying Influential Nodes in Complex Networks Based on Local Neighbor Contribution

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