The identification of influential nodes is one of the most significant and challenging research issues in network science. Many centrality indices have been established starting from topological features of networks. In this work, we propose a novel gravity model based on position and neighborhood (GPN), in which the mass of focal and neighbor nodes is redefined by the extended outspreading capability and modified k-shell iteration index, respectively. This new model comprehensively considers the position, local and path information of nodes to identify influential nodes. To test the effectiveness of GPN, a number of simulation experiments on nine real networks have been conducted with the aid of the susceptible–infected–recovered (SIR) model. The results indicate that GPN has better performance than seven popular methods. Furthermore, the proposed method has near linear time cost and thus it is suitable for large-scale networks.

中文翻译：

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GPN：一种基于位置和邻域的新型重力模型，用于识别复杂网络中的影响节点

影响节点的识别是网络科学中最重要和最具挑战性的研究问题之一。从网络的拓扑特征出发，建立了许多中心性指标。在这项工作中，我们提出了一种基于位置和邻域（GPN）的新型重力模型，其中焦点和相邻节点的质量通过扩展扩展能力重新定义并修改ķ-shell 迭代索引，分别。这种新模型综合考虑节点的位置、局部和路径信息来识别影响节点。为了测试 GPN 的有效性，借助易感-感染-恢复 (SIR) 模型，在九个真实网络上进行了大量模拟实验。结果表明，GPN 的性能优于七种流行的方法。此外，所提出的方法具有接近线性的时间成本，因此适用于大规模网络。