用张量方法统一建模动态图时空关系，更轻量高效
Learning Dynamic Graphs via Tensorized and Lightweight Graph Convolutional Networks
- 基于张量M-积实现时空信息联合传播
- 在4个真实数据集上优于现有最优模型
- 无需复杂变换与激活函数，内存占用大幅降低
动态图在众多现实场景中频繁出现，动态图卷积网络（DGCN）已成功用于其表示学习。然而，传统DGCN通常采用静态GCN与序列神经网络（SNN）分离建模空间与时间模式，这种解耦机制破坏了复杂的时空依赖性。为此，本文提出一种新型张量化轻量图卷积网络（TLGCN），核心包含：a) 基于张量M-积框架设计的新型时空信息传播方法，实现时空信息的联合传播；b) 基于此方法构建的张量化轻量图卷积网络，通过省略复杂特征变换与非线性激活显著降低模型内存占用。在四个真实数据集上的数值实验表明，所提TLGCN在动态图权重估计任务中优于当前最优模型。
原文摘要 · Abstract (English)
A dynamic graph (DG) is frequently encountered in numerous real-world scenarios. Consequently, A dynamic graph convolutional network (DGCN) has been successfully applied to perform precise representation learning on a DG. However, conventional DGCNs typically consist of a static GCN coupled with a sequence neural network (SNN) to model spatial and temporal patterns separately. This decoupled modeling mechanism inherently disrupts the intricate spatio-temporal dependencies. To address the issue, this study proposes a novel Tensorized Lightweight Graph Convolutional Network (TLGCN) for accurate dynamic graph learning. It mainly contains the following two key concepts: a) designing a novel spatio-temporal information propagation method for joint propagation of spatio-temporal information based on the tensor M-product framework; b) proposing a tensorized lightweight graph convolutional network based on the above method, which significantly reduces the memory occupation of the model by omitting complex feature transformation and nonlinear activation. Numerical experiments on four real-world datasets demonstrate that the proposed TLGCN outperforms the state-of-the-art models in the weight estimation task on DGs.
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