Shetty Ramya D, M Rashmi, Shetty Khyathi Rajesh, T Manoj
Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Sci Rep. 2025 Aug 25;15(1):31227. doi: 10.1038/s41598-025-15968-9.
Complex networks play a vital role in various real-world systems, including marketing, information dissemination, transportation, biological systems, and epidemic modeling. Identifying influential nodes within these networks is essential for optimizing spreading processes, controlling rumors, and preventing disease outbreaks. However, existing state-of-the-art methods for identifying influential nodes face notable limitations. For instance, Degree Centrality (DC) measures fail to account for global information, the K-shell method does not assign a unique ranking to nodes, and global measures are often computationally intensive. To overcome these challenges, this paper proposes a novel approach called Entropy Degree Distance Combination (EDDC), which integrates both local and global measures, such as degree, entropy, and distance. This approach incorporates local structure information by using entropy as a local metric and enhances the understanding of the overall graph structure by including path information as part of the global measure. This innovative method makes a substantial contribution to various applications, including virus spread modeling, viral marketing etc. The proposed approach is evaluated on six different benchmark datasets using well-known evaluation metrics and proved its efficiency.
复杂网络在各种现实世界系统中发挥着至关重要的作用，包括市场营销、信息传播、交通运输、生物系统和疫情建模等。识别这些网络中的有影响力节点对于优化传播过程、控制谣言和预防疾病爆发至关重要。然而，现有的用于识别有影响力节点的先进方法存在显著局限性。例如，度中心性（DC）度量未能考虑全局信息，K 壳方法没有为节点分配唯一排名，并且全局度量通常计算量很大。为了克服这些挑战，本文提出了一种名为熵度距离组合（EDDC）的新方法，该方法整合了局部和全局度量，如度、熵和距离。这种方法通过将熵用作局部度量来纳入局部结构信息，并通过将路径信息作为全局度量的一部分来增强对整体图结构的理解。这种创新方法对各种应用，包括病毒传播建模、病毒式营销等做出了重大贡献。所提出的方法使用著名的评估指标在六个不同的基准数据集上进行了评估，并证明了其有效性。
