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Conference Paper Fast Graph Learning for Traffic Prediction
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Authors
Sungsoo Kim, Moonyoung Chung
Issue Date
2023-12
Citation
International Conference on Big Data (Big Data) 2023, pp.6186-6188
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/BigData59044.2023.10386878
Abstract
Graph Convolutional Networks (GCNs) have been successfully utilized in modeling complex graph-structured data and have been applied in various applications such as epidemic tracing and so on. However, the training phase in GCNs faces challenges due to the computational overhead of repeated and inefficient aggregations based on graph convolution operations. We present a novel method called GCNIR that leverages reachability with incremental properties to efficiently compute diffusions in diffusion-based GCNs for node classification and traffic prediction tasks. The proposed method achieves significant speed-up gains for training semi-supervised models for node classification tasks on benchmark datasets. In addition, the proposed approach reduces the training time for diffusion-based GCN models in traffic prediction applications.