Visual graphs for image classification: does the structure affect performance?

• URL: http://arxiv.org/abs/2607.06295v1
• Date: Tue, 07 Jul 2026 14:01:44 GMT
• Title: Visual graphs for image classification: does the structure affect performance?
• Abstract summary: This work aims to conduct a systematic comparison of current graph construction techniques within the context of a fixed three-layer GCN architecture.<n>It demonstrates in particular how the network structure affects performance and provides an important methodological contribution regarding the computational stages preceding graph utilization.
• Score: 51.56484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Graph neural networks offer a good framework to face this aspect, but their effective use for visual tasks has been only partly explored and mainly starting from a limited perspective. This work aims to address this gap by conducting a systematic comparison of current graph construction techniques within the context of a fixed three-layer GCN architecture. Through an empirical study, it demonstrates in particular how the network structure affects performance and provides an important methodological contribution regarding the computational stages preceding graph utilization, which will be strongly influenced by the structure itself.

Related papers

• Graph is a Substrate Across Data Modalities [50.51414622183521]
  Graphs provide a natural representation of relational structure that arises across diverse domains.<n>Despite this ubiquity, graph structure is typically learned in a modality- and task-isolated manner.<n>We propose G-Substrate, a graph substrate framework that organizes learning around shared graph structures.
  arXiv  Detail & Related papers  (2026-01-29T22:46:02Z)
• Cross-View Topology-Aware Graph Representation Learning [0.0]
  We propose GraphTCL, a dual-view contrastive learning framework that integrates structural embeddings from GNNs with topological embeddings derived from persistent homology.<n>Experiments on benchmark datasets, including TU and OGB molecular graphs, demonstrate that GraphTCL consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-12-01T19:00:58Z)
• Graph Self-Supervised Learning with Learnable Structural and Positional Encodings [39.20899720477907]
  We introduce emphGenHopNet, a GNN framework that integrates a $k$-hop message-passing scheme.<n>We also propose a structural- and positional-aware GSSL framework that incorporates topological information throughout the learning process.<n>Our work significantly advances GSSL's capability in distinguishing graphs with similar local structures but different global topologies.
  arXiv  Detail & Related papers  (2025-02-22T14:10:06Z)
• Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting [50.181824673039436]
  We propose a Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge. It first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations.
  arXiv  Detail & Related papers  (2024-09-09T12:56:02Z)
• A Structural Feature-Based Approach for Comprehensive Graph Classification [1.5020330976600735]
  We propose a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications.
  arXiv  Detail & Related papers  (2024-08-10T07:59:43Z)
• Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks [0.9463895540925061]
  We introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN) It not only models the graph structure but also extracts generalized unsupervised features to enhance classification performance. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method.
  arXiv  Detail & Related papers  (2024-03-24T06:28:54Z)
• Digital Fingerprinting of Microstructures [44.139970905896504]
  Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. Here, we consider microstructure classification and utilise the resulting features over a range of related machine learning tasks. In particular, methods that leverage transfer learning with convolutional neural networks (CNNs), pretrained on the ImageNet dataset, are generally shown to outperform other methods.
  arXiv  Detail & Related papers  (2022-03-25T15:40:44Z)
• Graph Information Bottleneck [77.21967740646784]
  Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task. We show that our proposed models are more robust than state-of-the-art graph defense models.
  arXiv  Detail & Related papers  (2020-10-24T07:13:00Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)
• Affinity Graph Supervision for Visual Recognition [35.35959846458965]
  We propose a principled method to supervise the learning of weights in affinity graphs. Our affinity supervision improves relationship recovery between objects, even without manually annotated relationship labels. We show that affinity learning can also be applied to graphs built from mini-batches, for neural network training.
  arXiv  Detail & Related papers  (2020-03-19T23:52:51Z)

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.