A group behavior prediction model based on sparse representation and complex message interactions

Abstract

In view of the high propagation space and the complex networking of rumors, this paper proposes a group behavior prediction model based on sparse representation and interaction of complex messages. Firstly, to solve the difficulty in model training caused by the high dimension and complexity of rumor space, sparse representation is considered as the theoretical basis to construct sparse vectors for user node features, and to construct the node feature prediction submodel. Secondly, aiming at the dynamic interactive behavior among complex messages in the rumor space, the driving force of complex messages is quantified with the evolutionary game, the dynamic rumor propagation network is reconstructed, and the structure attribute prediction submodel is constructed. Finally, considering the advantages of model fusion in improving the generalization ability of the single model, the node feature prediction submodel—Submodel Based on SRC and the structure attribute prediction submodel—Submodel Based on Node2Vec are fused. Meanwhile, a dynamic group behavior prediction model under the influence of complex messages is constructed for the time-sensitive nature of rumor propagation. The experimental results show that the model not only effectively explores the interaction between complex messages but also accurately predicts the group behavior and depicts the rules of rumor propagation.