Dynamic Graph Node Classification via Time Augmentation

• URL: http://arxiv.org/abs/2212.03449v1
• Date: Wed, 7 Dec 2022 04:13:23 GMT
• Title: Dynamic Graph Node Classification via Time Augmentation
• Authors: Jiarui Sun, Mengting Gu, Chin-Chia Michael Yeh, Yujie Fan, Girish Chowdhary, Wei Zhang
• Abstract summary: We propose the Time Augmented Graph Dynamic Neural Network (TADGNN) framework for node classification on dynamic graphs. TADGNN consists of two modules: 1) a time augmentation module that captures the temporal evolution of nodes across time structurally, creating a time-augmentedtemporal graph, and 2) an information propagation module that learns the dynamic representations for each node across time using the constructed time-augmented graph. Experimental results demonstrate that TADGNN framework outperforms several static and dynamic state-of-the-art (SOTA) GNN models while demonstrating superior scalability.
• Score: 15.580277876084873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Node classification for graph-structured data aims to classify nodes whose labels are unknown. While studies on static graphs are prevalent, few studies have focused on dynamic graph node classification. Node classification on dynamic graphs is challenging for two reasons. First, the model needs to capture both structural and temporal information, particularly on dynamic graphs with a long history and require large receptive fields. Second, model scalability becomes a significant concern as the size of the dynamic graph increases. To address these problems, we propose the Time Augmented Dynamic Graph Neural Network (TADGNN) framework. TADGNN consists of two modules: 1) a time augmentation module that captures the temporal evolution of nodes across time structurally, creating a time-augmented spatio-temporal graph, and 2) an information propagation module that learns the dynamic representations for each node across time using the constructed time-augmented graph. We perform node classification experiments on four dynamic graph benchmarks. Experimental results demonstrate that TADGNN framework outperforms several static and dynamic state-of-the-art (SOTA) GNN models while demonstrating superior scalability. We also conduct theoretical and empirical analyses to validate the efficiency of the proposed method. Our code is available at https://sites.google.com/view/tadgnn.

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