Rumor Containment in Hypergraph Representation of Social Networks: A Deep Reinforcement Learning-Based Solution

Existing solutions for rumor containment typically model social networks as regular graphs, focusing solely on dyadic relationships between pairs of individuals. However, these solutions overlook the crowd influence, which arises from higher order relationships involving multiple individuals. This crowd influence is a distinct concept that cannot be substituted by the cumulative effect of individual influences through dyadic relationships. Therefore, it is important to consider the impact of crowd influence when modeling influence diffusion in the network. Moreover, traditional rumor containment methods lack generalization capacity. These methods require complete reexecution of algorithms whenever the target network changes, rendering them less efficient. In this work, we model the network as a hypergraph to effectively capture the crowd influence through higher order social relationships. We propose RCDRL-H, a deep reinforcement learning framework capable of constructing a trained model for containing rumors in previously unseen networks. Additionally, we introduce hyper-structure2vec, a node embedding technique for hypergraphs, and an efficient rumor containment estimation function that eliminates the need for computationally expensive Monte Carlo simulations during training. Experiments carried out on real-world social networks demonstrate that improved rumor containment can be achieved by treating the network as a hypergraph. It has also been observed that the achieved rumor containment is close to that of the greedy approach. Moreover, using the new estimation function significantly reduces training time.