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.

提出了一种新的多角度记忆增强网络 (Multi-perspective Memory Enhanced Network, MMEN) 来识别社交网络中的关键节点，从多个角度挖掘关键节点并利用记忆网络存储历史信息，综合考虑用户属性和传播结构的两个传播网络的节点影响，以选择最终的关键节点，实验证明该方法明显优于以前的方法。