Discovering and tracking influencer-influencee relationships between online communities

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.