DeltaGNN introduces an information flow control mechanism that uses a new connectivity measure, the information flow score, to mitigate over‑smoothing and over‑squashing in Graph Neural Networks. This approach enables linear computational and memory overhead while effectively capturing both short‑range and long‑range node interactions. Experiments on ten diverse real‑world datasets demonstrate superior performance with limited computational complexity.
By Kevin Mancini, Islem Rekik
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.
By Antonin Joly, Nicolas Keriven, Aline Roumy
Scaffold is a new unsupervised graph sparsification framework for graph neural networks that uses support graph theory preconditioners to jointly control dilation and congestion, thereby preserving short communication paths while avoiding bottlenecks. It achieves superior aggregate ranking across 19 homophilic and heterophilic benchmarks, recovering or closely approaching full‑graph GNN performance with only 10%–50% of the original edges. The method reduces memory usage to less than half and cuts end‑to‑end training time, including sparsification overhead.
By Siddhartha Shankar Das, Sai Karthik Navuluru, S M Ferdous, Ryan A. Rossi, Baris Coskunuzer, Lakshman Tamil, Edoardo Serra, Alex Pothen, Robert Rallo, Mahantesh M Halappanavar
arXiv:2601. 02451v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test.
By Subhankar Mishra
arXiv:2607. 25169v1 Announce Type: cross Abstract: Message-passing graph neural networks are bounded by the 1-WL test and can miss topological structure that distinguishes non-isomorphic graphs.
By Woohyun Lee, Hogun Park
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
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:2607. 27767v1 Announce Type: new Abstract: Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques for even larger graphs.
By Robert Jankowski, Pedro Almagro-Blanco, Mari\'an Bogu\~n\'a, Melanie Weber, M. \'Angeles Serrano
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.
By Isuru Herath, Arin Gopakumar, Sharan Sahu
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.
By Louisa Cornelis, Johan Mathe, Louis Van Langendonck, Guillermo Bern\'ardez, Nina Miolane
arXiv:2606. 21333v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning on graph-structured data by iteratively propagating and aggregating information across edges.
By Hugo Attali, Rachid El Jouhri
HyPE-GT introduces a framework that generates learnable hyperbolic positional encodings for Graph Transformers, enabling the capture of complex hierarchical relationships in graph-structured data. Unlike traditional Euclidean encodings, HyPE’s hyperbolic encodings can be selected to suit specific downstream tasks and help mitigate oversmoothing in deep Graph Neural Networks. Experiments on molecular benchmarks and large-scale Open Graph Benchmark datasets demonstrate improved performance, while additional tests on Coauthor and Copurchase networks confirm HyPE’s effectiveness in controlling oversmoothing.
By Kushal Bose, Swagatam Das