Replication Data for: Longitudinal Network Centrality Using Incomplete Data

How does individuals’ influence in a large social network change? Social scientists have difficulty answering this question because measuring influence requires frequent observations of a population of individuals’ connections to each other, while sampling that social network removes information in a way that can bias inferences. This paper introduces a method to measure influence over time accurately from sampled network data. Ranking individuals by the sum of their connections’ connections — neighbor cumulative indegree centrality — preserves the rank influence ordering that would be achieved in the presence of complete network data, lowering the barrier to measuring influence accurately. The paper then shows how to measure that variable changes each day, making it possible to analyze when and why an individual’s influence in a network changes. This method is demonstrated and validated on 21 Twitter accounts in Bahrain and Egypt from early 2011. The paper then discusses how to use the method in domains such as voter mobilization and marketing.

个体在大型社交网络中的影响力会如何变化？社会科学家此前难以解答这一问题，原因在于，衡量影响力需要频繁观测个体间的连接关系，而对社交网络进行采样会丢失部分信息，进而可能导致推断出现偏差。本研究提出了一种方法，可从采样得到的网络数据中精准地随时间推移衡量个体影响力。以个体的连接对象的连接之和——即邻居累积入度中心性（neighbor cumulative indegree centrality）——对个体进行排序，可保留完整网络数据下的影响力排序结果，从而降低了精准衡量影响力的门槛。随后，本研究展示了如何逐日计算该变量的变化，使得分析网络中个体影响力的变化时机与动因成为可能。本研究在2011年初巴林与埃及的21个推特（Twitter）账号数据集中对该方法进行了演示与验证。最后，本研究还讨论了该方法在选民动员与市场营销等领域的应用路径。