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。

多角度增强记忆网络用于社交网络中关键节点的识别

Multi-perspective Memory Enhanced Network for Identifying Key Nodes in  Social Networks

Detecting rumors on social media is a very critical task with significant
implications to the economy, public health, etc. Previous works generally
capture effective features from texts and the propagation structure. However,
the uncertainty caused by unreliable relations in the propagation structure is
common and inevitable due to wily rumor producers and the limited collection of
spread data. Most approaches neglect it and may seriously limit the learning of
features. Towards this issue, this paper makes the first attempt to explore
propagation uncertainty for rumor detection. Specifically, we propose a novel
Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust
structural features. The model adaptively rethinks the reliability of latent
relations by adopting a Bayesian approach. Besides, we design a new edge-wise
consistency training framework to optimize the model by enforcing consistency
on relations. Experiments on three public benchmark datasets demonstrate that
the proposed model achieves better performance than baseline methods on both
rumor detection and early rumor detection tasks.

本文针对社交媒体上谣言检测的不确定性问题，首次提出了通过 Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) 探索传播不确定性以提高谣言检测性能，并采用新的边一致性训练框架优化模型的方法。在三个公共基准数据集上进行实验证明，所提出的模型在谣言检测和早期谣言检测任务上均表现优于基准方法。