Adaptive Graph Coarsening for Efficient GNN Training

• URL: http://arxiv.org/abs/2509.25706v1
• Date: Tue, 30 Sep 2025 03:05:04 GMT
• Title: Adaptive Graph Coarsening for Efficient GNN Training
• Authors: Rostyslav Olshevskyi, Madeline Navarro, Santiago Segarra,
• Abstract summary: We propose an adaptive graph coarsening method to jointly learn graph neural network (GNN) parameters and merge nodes via K-means clustering during training.<n>We validate our approach on both homophilic and heterophilic node classification datasets.
• Score: 45.44339759125884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an adaptive graph coarsening method to jointly learn graph neural network (GNN) parameters and merge nodes via K-means clustering during training. As real-world graphs grow larger, processing them directly becomes increasingly challenging and sometimes infeasible. Tailoring algorithms to large-scale data may sacrifice performance, so we instead consider graph reduction to decrease the amount of data used during training. In particular, we propose a method to simultaneously train a GNN and coarsen its graph by partitioning nodes via K-means clustering based on their embeddings. Unlike past graph coarsening works, our approach allows us to merge nodes during training. Not only does this preclude coarsening as a preprocessing step, but our node clusters can adapt to the learning task instead of relying solely on graph connectivity and features. Thus, our method is amenable to scenarios that are challenging for other methods, such as heterophilic data. We validate our approach on both homophilic and heterophilic node classification datasets. We further visualize relationships between node embeddings and their corresponding clusters to illustrate that our coarsened graph adapts to the learning task during training.

Related papers

• FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening [1.1345413192078595]
  This paper presents a novel approach to improve the scalability of Graph Neural Networks (GNNs) by reducing computational burden during the inference phase using graph coarsening.<n>Our study extends the application of graph coarsening for graph-level tasks, including graph classification and graph regression.<n>Results show that the proposed method achieves orders of magnitude improvements in single-node inference time compared to traditional approaches.
  arXiv  Detail & Related papers  (2024-10-19T06:27:24Z)
• Spectral Greedy Coresets for Graph Neural Networks [61.24300262316091]
  The ubiquity of large-scale graphs in node-classification tasks hinders the real-world applications of Graph Neural Networks (GNNs) This paper studies graph coresets for GNNs and avoids the interdependence issue by selecting ego-graphs based on their spectral embeddings. Our spectral greedy graph coreset (SGGC) scales to graphs with millions of nodes, obviates the need for model pre-training, and applies to low-homophily graphs.
  arXiv  Detail & Related papers  (2024-05-27T17:52:12Z)
• You do not have to train Graph Neural Networks at all on text-attributed graphs [25.044734252779975]
  We introduce TrainlessGNN, a linear GNN model capitalizing on the observation that text encodings from the same class often cluster together in a linear subspace. Our experiments reveal that our trainless models can either match or even surpass their conventionally trained counterparts.
  arXiv  Detail & Related papers  (2024-04-17T02:52:11Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• Learnable Graph Matching: A Practical Paradigm for Data Association [74.28753343714858]
  We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
  arXiv  Detail & Related papers  (2023-03-27T17:39:00Z)
• Training Graph Neural Networks on Growing Stochastic Graphs [114.75710379125412]
  Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. We propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon.
  arXiv  Detail & Related papers  (2022-10-27T16:00:45Z)
• Shift-Robust Node Classification via Graph Adversarial Clustering [43.62586751992269]
  Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. During testing-time, these algorithms assume no data shift. We propose Shift-Robust Node Classification (SRNC) to address these limitations.
  arXiv  Detail & Related papers  (2022-03-07T18:13:21Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Graph Neural Network with Curriculum Learning for Imbalanced Node Classification [21.085314408929058]
  Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of node labels. We propose a novel graph neural network framework with curriculum learning (GNN-CL) consisting of two modules.
  arXiv  Detail & Related papers  (2022-02-05T10:46:11Z)
• Scalable Graph Neural Networks for Heterogeneous Graphs [12.44278942365518]
  Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities.
  arXiv  Detail & Related papers  (2020-11-19T06:03:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.