Contextual Centrality: Going Beyond Network Structures
Contextual Centrality: Going Beyond Network Structures
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上下文中心性：超越网络结构
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Pentland
中科院分区：
文献类型：
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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序。然而，结构重要性可能不足以预测在广泛的应用中成功的传播，例如口碑营销和政治活动。特别是，具有高结构重要性的节点可能对扩散的目标起到负面作用。为了解决这个问题，我们提出了上下文中心性，它整合了结构位置、扩散过程，最重要的是，节点对扩散目标的贡献。我们对印度农村采用小额信贷和中国农村采用天气保险进行了实证分析。结果表明，与其他标准中心性测量相比，第一知情个体的情境中心性对最终采用结果具有更高的预测力。有趣的是，当扩散速率和最大特征值的乘积大于1且扩散周期较长时，上下文中心性与特征向量中心性成线性关系。这一近似值表明，背景中心性确定了个人较高的扩散率可能对级联收益产生负面影响的情景。在合成网络和真实网络上的进一步模拟表明，上下文中心性的优势在于，当$p lambda_1<；1$时，选择其本地邻域产生高级联回报的个体。在这种情况下，更强的同质性导致更高的级联收益。我们的结果表明，情境中心性捕捉了网络上更复杂的动态，并对信息传播、病毒式营销和政治竞选等应用程序具有重要影响。
Centrality is a fundamental network property which ranks nodes by their structural importance. However, structural importance may not suffice to predict successful diffusions in a wide range of applications, such as word-of-mouth marketing and political campaigns. In particular, nodes with high structural importance may contribute negatively to the objective of the diffusion. To address this problem, we propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, nodal contributions to the objective of the diffusion. We perform an empirical analysis of the adoption of microfinance in Indian villages and weather insurance in Chinese villages. Results show that contextual centrality of the first-informed individuals has higher predictive power towards the eventual adoption outcomes than other standard centrality measures. Interestingly, when the product of diffusion rate $p$ and the largest eigenvalue $lambda_1$ is larger than one and diffusion period is long, contextual centrality linearly scales with eigenvector centrality. This approximation reveals that contextual centrality identifies scenarios where a higher diffusion rate of individuals may negatively influence the cascade payoff. Further simulations on the synthetic and real-world networks show that contextual centrality has the advantage of selecting an individual whose local neighborhood generates a high cascade payoff when $p lambda_1 < 1$. Under this condition, stronger homophily leads to higher cascade payoff. Our results suggest that contextual centrality captures more complicated dynamics on networks and has significant implications for applications, such as information diffusion, viral marketing, and political campaigns.