Discovering and tracking influencer-influencee relationships between online communities

机译：发现和跟踪在线社区之间的影响者-影响者关系

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摘要

This paper addresses a new problem concerning the discovery and tracking of influencer-influencee relationships between communities in dynamic social networks. A weighted temporal multigraph is employed to represent the dynamics of the social networks. To discover and track influencer-influencee relationships over time, communities sharing common interests are first grouped together in meta-communities using a topic modeling approach. Then, influencer-influencee relationships are discovered and tracked using the transfer entropy causality method. Through extensive experiments on the DBLP research publication dataset, we empirically demonstrate the suitability of our model for the discovery of influencer-influencee relationships between communities and the tracking of such relationships over time.

机译：本文提出了一个新问题，涉及在动态社交网络中发现和跟踪社区之间的影响者-影响者关系。加权时间多重图被用来表示社交网络的动态。为了发现和跟踪一段时间内影响者与影响者之间的关系，首先使用主题建模方法将共享共同兴趣的社区分组到元社区中。然后，使用传递熵因果关系方法发现并跟踪影响者与影响者的关系。通过在DBLP研究出版物数据集上进行的广泛实验，我们从经验上证明了我们的模型适用于发现社区之间的影响者-影响者关系以及随着时间的推移跟踪此类关系的适用性。

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《IEEE International Conference on Data Science and Advanced Analytics》|2015年|1-9|共9页
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Chikhaoui Belkacem; Chiazzaro Mauricio; Wang Shengrui;
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Approximation algorithms; Data preprocessing; Entropy; Inference algorithms; Optimization; Semantics; Social network services;

机译：近似算法;数据预处理;熵;推理算法;优化;语义;社交网络服务;

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Discovering and tracking influencer-influencee relationships between online communities

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