An Event Detection Method for Social Networks Based on Evolution Fluctuations of Nodes

How can we discover and estimate major events in complex social networks? 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. However, the existing methods ignore the difference of the evolution fluctuations of nodes. In order to further improve the accuracy of event detection, this paper proposes an event detection method for social networks based on node evolution fluctuations (NodeED). It contains a node similarity index algorithm (SimJudge) and a microevolution fluctuation detection algorithm (MicroFluc). The main work is as follows: 1) based on particle swarm optimization algorithm, SimJudge is proposed to apply the values of different similarity indexes to quantify the evolution fluctuations of nodes, and the optimal similarity index is determined for each node and 2) microFluc is proposed to integrate the evolution fluctuations of different nodes and quantify the impacts of events in the evolutions of social networks. The results of comparisons with state-of-the-art methods using extensive experiments show that NodeED improves the event detection accuracy and has more advantages to detect events in social networks than other state-of-the-art methods.