GAEN: Graph Attention Evolving Networks
GAEN: Graph Attention Evolving Networks
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DOI：
10.24963/ijcai.2021/213
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发表时间：
2021-08
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影响因子：
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通讯作者：
Min Shi;Yu Huang;Xingquan Zhu;Yufei Tang;Zhuang Yuan;Jianxun Liu
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作者：
Min Shi;Yu Huang;Xingquan Zhu;Yufei Tang;Zhuang Yuan;Jianxun Liu
现实世界的网络系统通常呈现出动态特性，其网络节点和拓扑结构会随着时间不断演变。在从动态网络中学习时，将所有时间网络相关联以充分捕捉节点之间的相似性/相关性是有益的。近期关于动态网络表示学习的工作通常独立地训练每个单一网络，并在不同时间步对网络学习施加相关性正则化。这种快照式方案未能利用时间网络之间的拓扑相似性进行渐进式训练。除了每个网络内的静态节点关系外，节点在时间网络序列中可能呈现出相似的变化模式（例如局部结构的变化）。静态节点结构和时间变化模式可以结合起来，以便更好地表征节点亲和性以进行统一的嵌入学习。在本文中，我们提出了图注意力演化网络（GAEN）用于动态网络嵌入，该方法保留了从节点的时间变化模式中得出的节点间的相似性。我们不是独立地为每个网络训练图注意力权重，而是允许模型权重根据它们各自的拓扑差异在所有时间网络中共享和演化。在四个现实世界的动态图上进行的实验和验证表明，GAEN在链路预测和节点分类任务中均优于现有技术。
Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.