Influential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc. This work aims to extend our formally defined T measure to present a new measure aiming to recognize the actor’s influence by the strength of attracting new important actors into a networked community. Therefore, we propose a model of the actor’s influence based on the attractiveness of the actor in relation to the number of other attractors with whom he/she has established connections over time. Using an empirically collected social network for the underlying graph, we have applied the above-mentioned measure of influence in order to determine optimal seeds in a simulation of influence maximization. We study our extended measure in the context of information diffusion because this measure is based on a model of actors who attract others to be active members in a community. This corresponds to the idea of the IC simulation model which is used to identify the most important spreaders in a set of actors.

中文翻译：

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使用吸引力模型对社交媒体网络中的演员进行排名。

社交媒体（如 Twitter 或 Facebook）中的有影响力的演员检测可以在收集特定主题的意见、提高营销效率、预测趋势等方面发挥重要作用。这项工作旨在扩展我们正式定义的 T 度量以提出一种新的度量目标通过吸引新的重要参与者进入网络社区的力量来识别参与者的影响力。因此，我们提出了一个演员影响力模型，该模型基于演员的吸引力与他/她随着时间的推移与之建立联系的其他吸引子的数量。使用经验收集的社交网络作为基础图，我们已经应用了上述影响测量，以便在影响最大化的模拟中确定最佳种子。我们在信息传播的背景下研究我们的扩展度量，因为该度量基于一个参与者模型，这些参与者吸引其他人成为社区中的活跃成员。这对应于 IC 仿真模型的想法，该模型用于识别一组参与者中最重要的吊具。