NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization

• URL: http://arxiv.org/abs/2503.02448v2
• Date: Thu, 06 Mar 2025 02:31:06 GMT
• Title: NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization
• Authors: Qiyi Wang, Yinning Shao, Yunlong Ma, Min Liu,
• Abstract summary: Graph neural architecture search (GraphNAS) has demonstrated advantages in mitigating performance degradation of graph neural networks (GNNs) due to distribution shifts.<n>We propose node-specific graph neural architecture search(NodeNAS), which aims to tailor distinct aggregation methods for different nodes.<n>We also propose adaptive aggregation attention based Multi-dim NodeNAS method(MNNAS), which learns an node-specific architecture customizer with good generalizability.
• Score: 6.069120487541545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural architecture search (GraphNAS) has demonstrated advantages in mitigating performance degradation of graph neural networks (GNNs) due to distribution shifts. Recent approaches introduce weight sharing across tailored architectures, generating unique GNN architectures for each graph end-to-end. However, existing GraphNAS methods do not account for distribution patterns across different graphs and heavily rely on extensive training data. With sparse or single training graphs, these methods struggle to discover optimal mappings between graphs and architectures, failing to generalize to out-of-distribution (OOD) data. In this paper, we propose node-specific graph neural architecture search(NodeNAS), which aims to tailor distinct aggregation methods for different nodes through disentangling node topology and graph distribution with limited datasets. We further propose adaptive aggregation attention based Multi-dim NodeNAS method(MNNAS), which learns an node-specific architecture customizer with good generalizability. Specifically, we extend the vertical depth of the search space, supporting simultaneous node-specific architecture customization across multiple dimensions. Moreover, we model the power-law distribution of node degrees under varying assortativity, encoding structure invariant information to guide architecture customization across each dimension. Extensive experiments across supervised and unsupervised tasks demonstrate that MNNAS surpasses state-of-the-art algorithms and achieves excellent OOD generalization.

Related papers

• Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings [4.343110120255532]
  We propose a novel benchmarking methodology for graph neural networks (GNNs) based on the graph alignment problem.<n>We frame this problem as a self-supervised learning task and present several methods to generate graph alignment datasets.<n>Our experiments indicate that anisotropic graph neural networks outperform standard convolutional architectures.
  arXiv  Detail & Related papers  (2025-05-19T13:22:17Z)
• DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts [70.21017141742763]
  Graph neural networks (GNNs) are gaining popularity for processing graph-structured data. Existing methods generally use a fixed number of GNN layers to generate representations for all graphs. We propose the depth adaptive mixture of expert (DA-MoE) method, which incorporates two main improvements to GNN.
  arXiv  Detail & Related papers  (2024-11-05T11:46:27Z)
• Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification [48.334100429553644]
  This paper proposes to design a joint graph data and architecture mechanism, which identifies important sub-architectures via the valuable graph data. To search for optimal lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural Architecture Search with Graph SparsIfication and Network Pruning (GASSIP) method. Our method achieves on-par or even higher node classification performance with half or fewer model parameters of searched GNNs and a sparser graph.
  arXiv  Detail & Related papers  (2024-06-24T06:53:37Z)
• Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks [25.12261412297796]
  Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. We propose a novel approach, namely collective structure knowledge-augmented graph neural network (CoS-GNN)
  arXiv  Detail & Related papers  (2024-05-17T08:50:00Z)
• GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy [21.553180564868306]
  GraphRARE is a framework built upon node relative entropy and deep reinforcement learning. An innovative node relative entropy is used to measure mutual information between node pairs. A deep reinforcement learning-based algorithm is developed to optimize the graph topology.
  arXiv  Detail & Related papers  (2023-12-15T11:30:18Z)
• Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
  We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
  arXiv  Detail & Related papers  (2023-05-18T07:27:18Z)
• A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features [61.92791503017341]
  Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data. The best models for such data types in most standard supervised learning settings with IID (non-graph) data are not easily incorporated into a GNN. Here we propose a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data.
  arXiv  Detail & Related papers  (2022-06-16T22:46:33Z)
• ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network [72.16255675586089]
  We propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T07:18:57Z)
• Improving Graph Neural Networks with Simple Architecture Design [7.057970273958933]
  We introduce several key design strategies for graph neural networks. We present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN) We show that the proposed model outperforms other state of the art GNN models and achieves up to 64% improvements in accuracy on node classification tasks.
  arXiv  Detail & Related papers  (2021-05-17T06:46:01Z)
• Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs [10.526065883783899]
  Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. We propose our GCN framework 'Deep Heterogeneous Graph Convolutional Network (DHGCN)' It takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away.
  arXiv  Detail & Related papers  (2021-05-03T06:24:27Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)

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.