arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).
arXiv:2505. 21285v5 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs.
arXiv:2607. 05017v1 Announce Type: cross Abstract: The performance of deep learning models crucially depends on the settings of hyperparameters like learning rate, initialization scale, and weight decay.
arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.
arXiv:2511. 11046v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have become an indispensable tool for analyzing relational data.
arXiv:2607. 26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints.
arXiv:2606. 14956v1 Announce Type: new Abstract: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.
arXiv:2606. 18317v1 Announce Type: new Abstract: Most graph neural network (GNN) cores rely on graph convolutions, typically implemented as message passing between direct (single-hop) neighbors.
arXiv:2606. 03495v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge.
arXiv:2608. 09031v1 Announce Type: new Abstract: Graph neural networks typically propagate information through repeated message-passing layers, coupling the distance over which information travels with the number of nonlinear transformations applied.
arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.
arXiv:2601. 19449v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations.
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.