arXiv:2601. 23207v2 Announce Type: replace-cross Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge.
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arXiv:2602.13106v2 Announce Type: replace-cross Abstract: In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a li...
The paper introduces an asynchronous message‑passing framework for Graph Neural Networks to mitigate oversquashing, a problem where distant nodes cannot effectively communicate due to structural bottlenecks. Unlike conventional synchronous updates, the method updates a centrality‑guided batch of nodes at each layer, allowing information to propagate sequentially and reducing the need for increased channel capacity. Experiments on six standard and two long‑range graph classification benchmarks show notable performance gains, including 5 % improvement on REDDIT‑BINARY and 4 % on Peptides‑struct.
arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...
Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations.
arXiv:2608. 02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers.
arXiv:2501.08547v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However...