Model Metamers Reveal Invariances in Graph Neural Networks

• URL: http://arxiv.org/abs/2510.17378v1
• Date: Mon, 20 Oct 2025 10:13:55 GMT
• Title: Model Metamers Reveal Invariances in Graph Neural Networks
• Authors: Wei Xu, Xiaoyi Jiang, Lixiang Xu, Dechao Tang,
• Abstract summary: In recent years, deep neural networks have been extensively employed in perceptual systems to learn representations endowed with invariances.<n>These networks aim to emulate the invariance mechanisms observed in the human brain.<n>However, studies in the visual and auditory domains have confirmed that significant gaps remain between the invariance properties of artificial neural networks and those of humans.
• Score: 8.901234530419387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, deep neural networks have been extensively employed in perceptual systems to learn representations endowed with invariances, aiming to emulate the invariance mechanisms observed in the human brain. However, studies in the visual and auditory domains have confirmed that significant gaps remain between the invariance properties of artificial neural networks and those of humans. To investigate the invariance behavior within graph neural networks (GNNs), we introduce a model ``metamers'' generation technique. By optimizing input graphs such that their internal node activations match those of a reference graph, we obtain graphs that are equivalent in the model's representation space, yet differ significantly in both structure and node features. Our theoretical analysis focuses on two aspects: the local metamer dimension for a single node and the activation-induced volume change of the metamer manifold. Utilizing this approach, we uncover extreme levels of representational invariance across several classic GNN architectures. Although targeted modifications to model architecture and training strategies can partially mitigate this excessive invariance, they fail to fundamentally bridge the gap to human-like invariance. Finally, we quantify the deviation between metamer graphs and their original counterparts, revealing unique failure modes of current GNNs and providing a complementary benchmark for model evaluation.

Related papers

• Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection [15.157616444432563]
  Graph fraud detection is crucial for identifying fraudulent behavior within graphs, benefiting various domains such as financial networks and social media.<n>Existing methods based on graph neural networks (GNNs) have succeeded considerably due to their effective expressive capacity for graph-structured data.<n>We propose Multi-scale Neighborhood Awareness Transformer (MANDATE), which alleviates the inherent inductive bias of GNNs.
  arXiv  Detail & Related papers  (2026-03-03T15:41:10Z)
• Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification [7.822666400307049]
  We propose a novel framework, overlap-aware meta-learning attention for hypergraph neural networks (OMA-HGNN)<n>First, we introduce a hypergraph attention mechanism that integrates both structural and feature similarities. Specifically, we linearly combine their respective losses with weighted factors for the HGNN model.<n>Second, we partition nodes into different tasks based on their diverse overlap levels and develop a multi-task Meta-Weight-Net (MWN) to determine the corresponding weighted factors.<n>Third, we jointly train the internal MWN model with the losses from the external HGNN model and train the external model with the weighted factors
  arXiv  Detail & Related papers  (2025-03-11T01:38:39Z)
• Generalization of Graph Neural Networks is Robust to Model Mismatch [84.01980526069075]
  Graph neural networks (GNNs) have demonstrated their effectiveness in various tasks supported by their generalization capabilities. In this paper, we examine GNNs that operate on geometric graphs generated from manifold models. Our analysis reveals the robustness of the GNN generalization in the presence of such model mismatch.
  arXiv  Detail & Related papers  (2024-08-25T16:00:44Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks [44.31777384413466]
  Graph neural networks (GNNs) have become increasingly popular for classification tasks on graph-structured data. In this paper, we focus on node-wise classification and explore the feature evolution through the lens of the "Neural Collapse" phenomenon. We show that even an "optimistic" mathematical model requires that the graphs obey a strict structural condition in order to possess a minimizer with exact collapse.
  arXiv  Detail & Related papers  (2023-07-04T23:03:21Z)
• Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals [7.6435511285856865]
  Graph neural networks (GNNs) are widely used in domains like social networks and biological systems. locality assumption of GNNs hampers their ability to capture long-range dependencies and global patterns in graphs. We propose a new inductive bias based on variational analysis, drawing inspiration from the Brachchronistoe problem.
  arXiv  Detail & Related papers  (2023-07-01T04:44:43Z)
• Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs [122.06927400759021]
  We study the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold. We prove non-asymptotic error bounds showing that convolutional filters and neural networks on these graphs converge to convolutional filters and neural networks on the continuous manifold.
  arXiv  Detail & Related papers  (2023-05-29T08:27:17Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• On the Prediction Instability of Graph Neural Networks [2.3605348648054463]
  Instability of trained models can affect reliability, reliability, and trust in machine learning systems. We systematically assess the prediction instability of node classification with state-of-the-art Graph Neural Networks (GNNs) We find that up to one third of the incorrectly classified nodes differ across algorithm runs.
  arXiv  Detail & Related papers  (2022-05-20T10:32:59Z)
• E(n) Equivariant Graph Neural Networks [86.75170631724548]
  This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs) In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance.
  arXiv  Detail & Related papers  (2021-02-19T10:25:33Z)
• Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing [88.30867628592112]
  Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. We show that existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. In order to preserve node proximities, we augment the existing GNNs with node representations.
  arXiv  Detail & Related papers  (2020-09-05T16:46:56Z)

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.