Interdisciplinary Center for Network Science and Applications (iCeNSA), Department of Computer Science and Engineering, University of Notre Dame.
Sci Rep. 2014 Nov 28;4:7236. doi: 10.1038/srep07236.
Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential importance of an individual in the future. However, current centrality is not necessarily an effective predictor of future centrality. While there are different measures of centrality, we focus on degree centrality in this paper. We develop a method that reconciles preferential attachment and triadic closure to capture a node's prominence profile. We show that the proposed node prominence profile method is an effective predictor of degree centrality. Notably, our analysis reveals that individuals in the early stage of evolution display a distinctive and robust signature in degree centrality trend, adequately predicted by their prominence profile. We evaluate our work across four real-world social networks. Our findings have important implications for the applications that require prediction of a node's future degree centrality, as well as the study of social network dynamics.
节点的中心度衡量其在网络中的相对重要性。中心度有许多应用，包括推断社交网络中个体的影响力或成功程度，以及由此产生的社交网络动态。虽然我们可以计算给定网络快照中任何节点的中心度，但许多应用也有兴趣了解个体未来的潜在重要性。然而，当前的中心度不一定是未来中心度的有效预测指标。虽然有不同的中心度度量方法，但我们在本文中专注于度中心度。我们开发了一种方法，将优先连接和三元闭合结合起来，以捕捉节点的突出度分布。我们表明，所提出的节点突出度分布方法是度中心度的有效预测指标。值得注意的是，我们的分析表明，处于进化早期的个体在度中心度趋势中表现出独特而稳健的特征，这些特征可以通过他们的突出度分布得到充分预测。我们在四个真实世界的社交网络中评估了我们的工作。我们的研究结果对需要预测节点未来度中心度的应用以及社交网络动态的研究具有重要意义。
