Influential Spreaders Identification in Complex Networks With TOPSIS and K-Shell Decomposition

Liu, Jiamiao; Ye, Shu; Fiumara, Giacomo; Meo, Pasquale De

doi:10.1109/tcss.2022.3148778

Cited by 13 publications

(1 citation statement)

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“…However, the urban PT network almost conforms to the characteristics of all network models and also has the corresponding drawbacks of various models, such as the possibility of complete paralysis if a large key hub is attacked. Therefore, to quickly separate and evaluate the importance of each traffic node, the K-shell decomposition (K-shell) method is used to analyze, peel off, and classify nodes at various levels in the traffic network, thereby providing important data support and reference for the maintenance of key nodes and safety assurance plans [22]. The K-shell algorithm process is shown in Figure 3.…”

Section: A Cn and K-shell Algorithmmentioning

confidence: 99%

Application of K-Shell Algorithm in Key Point Identification and Cascading Failure in Urban Public Safety Transportation Network

Xiao,

Ren

2024

IEEE Access

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The rapid development of modern urban public transportation and people's demand for efficient transportation have made intelligent management of urban public transportation networks a hot research field. In view of this, a composite complex traffic network node classification algorithm model is established based on the shell decomposition algorithm combined with a cascading failure model. The final research findings denote that the classification refinement level of the comprehensive shell decomposition value is about 50% higher than that of the WK-shell decomposition value. The comprehensive shell decomposition algorithm, which integrates global and local influence factors, can almost achieve one shell decomposition value per node in classification. The number of layers with nodes below 30 in the actual transportation network dataset test accounts for about 98% of the total number under the improved comprehensive shell decomposition algorithm, and roughly 50% under the composite shell decomposition algorithm, indicating that the accuracy of the former algorithm is higher. When the relationship ratio in robustness testing is 1:1:1, the average comprehensive shell decomposition value is at a higher position, which is about 3% higher than the WK-shell decomposition value and about 5% higher than the traditional shell decomposition value. In addition, when the proportions are 5:1:1, 1:5:1, and 1:1:5, the failure points tested under the comprehensive shell decomposition value are still the most, indicating that the comprehensive shell decomposition value considers more factors, is more cautious, and is closer to the actual situation. Through experimental data, the proposed composite complex traffic network node classification algorithm model, which integrates improved shell decomposition algorithm and cascading failure model, has certain practicality and feasibility.

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Section: A Cn and K-shell Algorithmmentioning

confidence: 99%