An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent

Abstract

How can we discover and estimate major events in complex social networks? Even with ever enlarging networks and data scale? Event detection and evaluation in social networks provide an effective solution, which has become the critical basis for many real applications, such as crisis management and decision making. The existing event detection methods mainly focus on text analysis that is limited in social media content and graphic feature statistic that needs calculate vast variables. Can we find an efficient way for generalized social networks with limited topology information? In this paper, a novel hybrid quantum swarm intelligence indexing method (HQSII) from the perspective of link prediction is proposed for the first time, which includes an optimal weight algorithm (OWA) and a fluctuation detection algorithm (FDA). The innovations behind HQSII lie in three aspects: (1) The mixed index that can universally describe the social network evolutions is proposed firstly, which explores the cooperation of different independent similarity indexes. OWA is further proposed to determine the optimal mixed index that achieves higher link prediction accuracy and better network evolution description than other independent and mixed similarity indexes. (2) To better avoid the interferences of routine network evolution fluctuations, the otherness of micro node evolutions is considered into link prediction. FDA is further proposed to quantify the abnormal fluctuations caused by events. (3) Based on OWA and FDA, HQSII is proposed for all the generalized social networks, which detects events by discovering abnormal fluctuations and evaluates events by analyzing fluctuation trends. Extensive experiments on theoretical and real-world social networks show that HQSII can accurately detect events and quantitatively evaluate event impacts in social networks with single and multiple events.