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

arXiv: https://arxiv.org/abs/2403.15235 · HTML: https://mlsanity.com/paper/2403.15235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social Networks](https://mlsanity.com/paper/2111.00463/) (2021)
- [A discount strategy in word-of-mouth marketing and its assessment](https://mlsanity.com/paper/1704.06910/) (2017)
- [Fast influencers in complex networks](https://mlsanity.com/paper/1903.06367/) (2019)

## Abstract (arXiv)

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.