Hugging Face Trending Papers
Node-level Graph Neural Architecture Search Framework
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arXiv Machine Learning
Oct 8
Node-level Graph Neural Architecture Search Framework
The paper introduces N-GNAS, a Node-Level Graph Neural Architecture Search framework that selects distinct network architectures for different node subsets during feature updates. It incorporates a contrastive learning loss to better separate features across categories. Experiments on eight node and graph classification datasets show that N-GNAS outperforms existing GNAS methods and hand‑crafted GNNs, achieving 78.26% accuracy on CiteSeer.
arXiv Machine Learning
Aug 4
CoRe-GNN: Multilevel Message passing on Coarsened graphs
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.
Hugging Face Trending Papers
Aug 3
CoRe-GNN: Multilevel Message passing on Coarsened graphs
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 Machine Learning
Jun 2
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.
arXiv AI
Jun 11
GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).
arXiv Machine Learning
4d ago
DIFFNAS: Predictor-Free Neural Architecture Search via Diffusion Model and Zero-Cost Evaluation
DIFFNAS is a new neural architecture search framework that eliminates the need for performance predictors by using a discrete graph diffusion model conditioned on zero‑cost performance proxies such as MeCo and SWAP. The method achieves state‑of‑the‑art results on NAS‑Bench‑201 for CIFAR‑10 and CIFAR‑100, matching optimal architectures while cutting search cost by up to 17.5× compared to DiffusionNAG. Although its accuracy on the MobileNetV3 search space is lower than DiffusionNAG, the results demonstrate the potential of diffusion models combined with training‑free metrics for scalable architecture search.