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COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships.
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels
Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios.
Learning on Graphs under Label Noise
Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise.
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
