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.
In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing...
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:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.
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.
The study investigates how different message‑passing (MP) layers affect graph‑level regression performance in Graph Neural Networks (GNNs). By replacing the MP component in four existing GNN regressors with ten varied configurations—including convolutional, isomorphism‑based, and attention‑based designs—the authors evaluate performance across eleven neural‑network graph datasets, measuring metrics such as rank correlation, prediction error, top‑k retrieval, latency, and memory usage. Results show that the choice of MP layer significantly influences outcomes, with the optimal layer depending on graph size, training‑set size, and regression objective; classical layers like GEN, k‑GNN, and PNA outperform attention‑based layers on small graphs, while GATv2 excels on datasets with few large graphs.
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.
Deep learning models have emerged in machine learning and related fields, demonstrating astonishing performance in various visual tasks. Despite their great success, however, these models are unable to fully encode intrinsic visual structures, and often ignore the spatial, topological, and semantic information contained within an image.
The paper introduces RGC‑Net, a Reservoir‑Based Graph Convolutional Network that combines fixed‑random reservoir dynamics with a structured convolutional framework for graph learning. It addresses limitations of existing reservoir‑based GNNs by adding a leaky integrator for better feature retention and a robust, adaptable architecture for graph classification and generation. Experiments demonstrate state‑of‑the‑art performance on classification and generative tasks, including dynamic brain connectivity, with faster convergence and reduced over‑smoothing.
arXiv:2611.08379v1 Announce Type: new Abstract: Different optimizers impose different geometries on parameter updates. Adaptive methods such as Adam and AdamW use coordinate-wise updates, while matri...
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).
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.
Graph-Dependent Pooling (GDPool) introduces a dynamic approach to hierarchical graph pooling, allowing each graph to determine its own pooling granularity rather than relying on fixed cluster numbers or ratios. The method employs self-attention to select graph-specific anchors and uses soft node assignment to build coarser graphs, addressing optimization conflicts identified through gradient alignment analysis. Experiments on metal‑organic framework and molecular graph datasets demonstrate that GDPool outperforms existing methods in predictive accuracy, modularity, and exhibits stable behavior across various hyperparameters.