Influential nodes identification in complex networks based on global and local information

Yang, Young-Hoon; Hu, Min; Huang, Tai-Yu

doi:10.1088/1674-1056/ab969f

Cited by 31 publications

(33 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GLI method [19] contains both global location information and local structure information. First, considering that the k-shell decomposition method divides many nodes into the same layer, an improved k-shell decomposition method (Iks) was defined as follows:…”

Section: Gli Methodsmentioning

confidence: 99%

“…Sheng et al [18] combined the global and local structural characteristics of the network, global information reflecting the proximity to other nodes in the network, and local information as the contribution value of the nearest neighbor node to the measured node. Yang et al [19] first proposed an improved k-shell decomposition method based on the k-shell value and the number of iterations of node removal in the k-shell decomposition, and then combined the improved method with degree centrality and the shortest path length to characterize the node influence. Zareie et al [20] proposed an improved clustering ranking approach, which takes the common hierarchical structure of nodes and their neighborhood set into account.…”

Section: Introductionmentioning

confidence: 99%

“…To verify the effectiveness of the proposed method, this paper uses the SIR model [22,23] to perform 1000 independent simulations on multiple real networks to obtain the information dissemination ability of nodes. The proposed method in this paper is compared with degree centrality, betweenness centrality, closeness centrality, area density centrality [15], GM method [16], CLD method [17], and GLI method [19] in terms of discrimination and accuracy. The experimental results show that the proposed method in this paper can distinguish the influence among nodes and improve the recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Zhu

Wang

2021

Symmetry

View full text Add to dashboard Cite

Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

show abstract

Section: Gli Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Zhu

Wang

2021

Symmetry

View full text Add to dashboard Cite

show abstract

“…For example, degree centrality only considers node's influence capability from local information. It does not make an in-depth quantification of their surrounding environment, such as target node's position and neighborhood attributes within multisteps [21][22][23]. BC and CC are both based on the shortest distance between node pairs, reflecting the control force of network flow.…”

Section: Literature Reviewmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Similarly, in the traffic system network 7 , food chain network 8 , drug network 9 , and so on. There are a good number of studies have been proposed and deployed in the field of complex network on the identification of influential nodes 10 – 12 , 12 – 20 where identification of most important nodes from local and global perspectives is worth mentioning 21 . Although closeness centrality (CC) and betweenness centrality (BC) 22 are path-based indicators that consider the global structure of the network to identify the influence of nodes.…”

mentioning

confidence: 99%

Identification of nodes influence based on global structure model in complex networks

Ullah

Wang

Sheng

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).

show abstract

Influential nodes identification in complex networks based on global and local information

Cited by 31 publications

References 31 publications

Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

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

Identification of nodes influence based on global structure model in complex networks

Contact Info

Product

Resources

About