Influential User Subscription on Time-Decaying Social Streams

Influence maximization which asks for k-size seed set from a social network such that maximizing the influence over all other users (called inlfuence spread) has widely aŠracted aŠention due to its significant applications in viral markeing and rumor control. In real world scenarios, people are interested in the most influential users in particular topics, and want to subscribe the topicsof-interests over social networks. In this paper, we formulate the problem of influential users subscription on time-decaying social stream, which asks for maintaining the k-size inlfuential users sets for each topic-aware subscipriton queries. We first analyize the widely adopted sliding window model and propose a newly timedecaying influence model to overcome the shortages when calculating the influence over social stream. Developed from sieve based streaming algorithm, we propose an effecient algorithm to support the calculation of time-decaying influence over dynamically updating social networks. Using information among subscriptions, we then construct the Prefix Tree Structure to allow us minimizing the times of calculating influece of each update and easily maintained. Pruning techniques are also applied to the Prefix Tree to optimize the performance of social stream update. Our approch ensures a 1 2 − ε approximation ratio. Experimental results show that our approach significantly outperforms the baseline approaches in effeciency and result quality. ACM Reference format: Xin Yang and Ju Fan. 2016. Influential User Subscription on Time-Decaying Social Streams. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 9 pages. DOI: 0000001.0000001