Lü Linyuan, Zhou Tao, Zhang Qian-Ming, Stanley H Eugene
Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China.
CompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China.
Nat Commun. 2016 Jan 12;7:10168. doi: 10.1038/ncomms10168.
Identifying influential nodes in dynamical processes is crucial in understanding network structure and function. Degree, H-index and coreness are widely used metrics, but previously treated as unrelated. Here we show their relation by constructing an operator , in terms of which degree, H-index and coreness are the initial, intermediate and steady states of the sequences, respectively. We obtain a family of H-indices that can be used to measure a node's importance. We also prove that the convergence to coreness can be guaranteed even under an asynchronous updating process, allowing a decentralized local method of calculating a node's coreness in large-scale evolving networks. Numerical analyses of the susceptible-infected-removed spreading dynamics on disparate real networks suggest that the H-index is a good tradeoff that in many cases can better quantify node influence than either degree or coreness.
识别动态过程中的有影响力节点对于理解网络结构和功能至关重要。度、H指数和核数是广泛使用的指标,但以前被视为互不相关。在这里,我们通过构造一个算子来展示它们之间的关系,根据这个算子,度、H指数和核数分别是序列的初始、中间和稳态。我们得到了一族可用于衡量节点重要性的H指数。我们还证明,即使在异步更新过程下,也能保证收敛到核数,这使得在大规模演化网络中可以采用分散式局部方法来计算节点的核数。对不同真实网络上易感-感染-移除传播动力学的数值分析表明,H指数是一个很好的折衷指标,在许多情况下,它比度或核数能更好地量化节点影响力。