Multi-perspective Memory Enhanced Network for Identifying Key Nodes in Social Networks
arxiv(2024)
摘要
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.
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