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.
By Yuhang Song, Naima Abrar Shami, Romaric Duvignau, Vasiliki Kalavri
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.
By Lintao Yanga, Sirui Lia, Yaqing Wang, Pietro Li\`o, Xu Shen, Baisong Liu, Chengbin Peng
arXiv:2606. 24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution.
By Abdelkader Dairi, Fouzi Harrou, Ying Sun
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.
By Muhammad Fetrat Qharabagh, Artur Back de Luca, George Giapitzakis, Kimon Fountoulakis
arXiv:2607. 19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells.
By William Xing, Stephanie Yang, Aarush Bandemegal, Anushree Misra, Ananya Kalapatapu, Brennan Lagasse, Kevin Zhu
Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data.