Mining subcascade features for cascade outbreak prediction in big networks

An information cascade occurs when a person observes the actions of others and then engages in the same acts. Cascades may break out if a large population of nodes in the network get affected. The outbreaks of cascades will often bring influential events, which leads to an open research problem: how to accurately predict the cascading outbreaks in social networks? Although there have been some existing works on cascading outbreak prediction, they ignore the structure information of cascades. In this paper, we propose to use subcascades as features for cascade outbreak prediction. We use frequent sequential pattern mining to extract subcascades and then propose a max-margin based classifier to select at most B features for prediction. The proposed model is empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.