Identifying influential nodes in complex networks through the k-shell index and neighborhood information

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-11-15 DOI:10.1016/j.jocs.2024.102473

Shima Esfandiari, Mohammad Reza Moosavi

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Abstract

Identifying influential nodes is crucial in network science for controlling diseases, sharing information, and viral marketing. Current methods for finding vital spreaders have problems with accuracy, resolution, or time complexity. To address these limitations, this paper presents a hybrid approach called the Bubble Method (BM). First, the BM assumes a bubble with a radius of two surrounding each node. Then, it extracts various attributes from inside and near the surface of the bubble. These attributes are the k-shell index, k-shell diversity, and the distances of nodes within the bubble from the central node. We compared our method to 12 recent ones, including the Hybrid Global Structure model (HGSM) and Generalized Degree Decomposition (GDD), using the Susceptible–Infectious–Recovered (SIR) model to test its effectiveness. The results show the BM outperforms other methods in terms of accuracy, correctness, and resolution. Its low computational complexity renders it highly suitable for analyzing large-scale networks.

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通过 K 壳指数和邻域信息识别复杂网络中的有影响力节点

在网络科学中，识别有影响力的节点对于控制疾病、共享信息和病毒营销至关重要。目前寻找重要传播者的方法在准确性、分辨率或时间复杂性方面存在问题。为了解决这些局限性，本文提出了一种名为 "气泡法"（BM）的混合方法。首先，气泡法假定每个节点周围都有一个半径为 2 的气泡。然后，从气泡内部和表面附近提取各种属性。这些属性包括 k 壳指数、k 壳多样性以及气泡内节点与中心节点的距离。我们使用易感-感染-恢复（SIR）模型，将我们的方法与包括混合全局结构模型（HGSM）和广义度分解（GDD）在内的 12 种最新方法进行了比较，以检验其有效性。结果表明，BM 在准确性、正确性和分辨率方面都优于其他方法。它的计算复杂度低，非常适合分析大规模网络。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).