A Realistic Polar Influence Propagation Model for Location based Social Networks

Abstract

Location-based social networks (LBSNs) have gained significant popularity in recent years. LBSNs are a special type of social networks bridging the gap between online social networks and offline physical world. It is due to this special property that influence propagation in LBSNs have become an interesting research topic in past few years. Significant research work discussing the dynamics of influence propagation in LBSNs has been done so far. But the current influence propagation models still lack important parameters which make the model more aligned with real world scenarios. Based on existing works, this research paper proposes an improved realistic influence propagation model using mobile crowdsourced data obtained from a renowned LBSN. The proposed influence model incorporates polarity of influence associating it with positive or negative state. A new interest-match coefficient is also proposed which is based on real-world similarity between interests. The experimental results indicate that the proposed influence propagation model is meaningful and better aligned with reality.