Contextual centrality: going beyond network structure
Contextual centrality: going beyond network structure
复制标题
情境中心性：超越网络结构
DOI：
10.1038/s41598-020-62857-4
复制
发表时间：
2018
影响因子：
4.6
通讯作者：
A. Pentland
中科院分区：
文献类型：
--
作者：
Yan Leng;Yehonatan Sella;Rodrigo Ruiz;A. Pentland
中心性是一种基本的网络属性，它根据节点的结构重要性对节点进行排名。然而，仅靠网络结构可能无法预测在许多应用程序中的成功传播，例如病毒式营销和政治竞选。我们提出了语境中心性，它整合了结构位置、扩散过程，最重要的是相关节点特征。它很好地概括了标准中心性度量，并与之相关。我们测试了情境中心性在预测采用小额信贷和天气保险的最终结果方面的有效性。我们的实证分析表明，第一知情个体的情境中心性比其他标准中心性测量具有更高的预测能力。进一步的仿真表明，当扩散发生在局部时，上下文中心性可以识别出其局部邻域贡献为正的节点。当扩散在全球范围内发生时，语境中心性标志着扩散是否会产生负面后果。与传统的中心性测量相比，上下文中心性捕捉到了网络上更复杂的动态，并对基于网络的干预具有重要影响。
Centrality is a fundamental network property that ranks nodes by their structural importance. However, the network structure alone may not predict successful diffusion in many applications, such as viral marketing and political campaigns. We propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, relevant node characteristics. It nicely generalizes and relates to standard centrality measures. We test the effectiveness of contextual centrality in predicting the eventual outcomes in the adoption of microfinance and weather insurance. Our empirical analysis shows that the contextual centrality of first-informed individuals has higher predictive power than that of other standard centrality measures. Further simulations show that when the diffusion occurs locally, contextual centrality can identify nodes whose local neighborhoods contribute positively. When the diffusion occurs globally, contextual centrality signals whether diffusion may generate negative consequences. Contextual centrality captures more complicated dynamics on networks than traditional centrality measures and has significant implications for network-based interventions.