Multi-perspective Memory Enhanced Network for Identifying Key Nodes in Social Networks
This work addresses the challenge of identifying key nodes for false information blocking in social networks, but it appears incremental as it builds on existing methods by adding multi-perspective and memory enhancements.
The paper tackles the problem of identifying key nodes in social networks to block false information by proposing a Multi-perspective Memory Enhanced Network (MMEN) that mines nodes from multiple perspectives and uses memory networks for generalization, resulting in significant outperformance over previous methods in experiments.
Identifying key nodes in social networks plays a crucial role in timely blocking false information. Existing key node identification methods usually consider node influence only from the propagation structure perspective and have insufficient generalization ability to unknown scenarios. In this paper, we propose a novel Multi-perspective Memory Enhanced Network (MMEN) for identifying key nodes in social networks, which mines key nodes from multiple perspectives and utilizes memory networks to store historical information. Specifically, MMEN first constructs two propagation networks from the perspectives of user attributes and propagation structure and updates node feature representations using graph attention networks. Meanwhile, the memory network is employed to store information of similar subgraphs, enhancing the model's generalization performance in unknown scenarios. Finally, MMEN applies adaptive weights to combine the node influence of the two propagation networks to select the ultimate key nodes. Extensive experiments demonstrate that our method significantly outperforms previous methods.