A Class-Aware Representation Refinement Framework for Graph Classification

• URL: http://arxiv.org/abs/2209.00936v2
• Date: Thu, 6 Jun 2024 09:50:54 GMT
• Title: A Class-Aware Representation Refinement Framework for Graph Classification
• Authors: Jiaxing Xu, Jinjie Ni, Yiping Ke,
• Abstract summary: We propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification. CARE computes simple yet powerful class representations and injects them to steer the learning of graph representations towards better class separability. Our experiments with 11 well-known GNN backbones on 9 benchmark datasets validate the superiority and effectiveness of CARE over its GNN counterparts.
• Score: 8.998543739618077
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect of graph-level relationships, and the generalization issue. Each graph is treated separately in GNN message passing/graph pooling, and existing methods to address overfitting operate on each individual graph. This makes the graph representations learnt less effective in the downstream classification. In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification. CARE computes simple yet powerful class representations and injects them to steer the learning of graph representations towards better class separability. CARE is a plug-and-play framework that is highly flexible and able to incorporate arbitrary GNN backbones without significantly increasing the computational cost. We also theoretically prove that CARE has a better generalization upper bound than its GNN backbone through Vapnik-Chervonenkis (VC) dimension analysis. Our extensive experiments with 11 well-known GNN backbones on 9 benchmark datasets validate the superiority and effectiveness of CARE over its GNN counterparts.

Related papers

• Graph Classification via Reference Distribution Learning: Theory and Practice [24.74871206083017]
  This work introduces Graph Reference Distribution Learning (GRDL), an efficient and accurate graph classification method. GRDL treats each graph's latent node embeddings given by GNN layers as a discrete distribution, enabling direct classification without global pooling. Experiments on moderate-scale and large-scale graph datasets show the superiority of GRDL over the state-of-the-art.
  arXiv  Detail & Related papers  (2024-08-21T06:42:22Z)
• SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling [25.555741218526464]
  Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. We propose a concatenation-based graph convolution mechanism that injectively updates node representations. We also design a novel graph pooling module, called WL-SortPool, to learn important subgraph patterns in a deep-learning manner.
  arXiv  Detail & Related papers  (2024-04-21T13:11:59Z)
• Learning to Reweight for Graph Neural Network [63.978102332612906]
  Graph Neural Networks (GNNs) show promising results for graph tasks. Existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data. We propose a novel nonlinear graph decorrelation method, which can substantially improve the out-of-distribution generalization ability.
  arXiv  Detail & Related papers  (2023-12-19T12:25:10Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Training Free Graph Neural Networks for Graph Matching [103.45755859119035]
  TFGM is a framework to boost the performance of Graph Neural Networks (GNNs) based graph matching without training. Applying TFGM on various GNNs shows promising improvements over baselines.
  arXiv  Detail & Related papers  (2022-01-14T09:04:46Z)
• Imbalanced Graph Classification via Graph-of-Graph Neural Networks [16.589373163769853]
  Graph Neural Networks (GNNs) have achieved unprecedented success in learning graph representations to identify categorical labels of graphs. We introduce a novel framework, Graph-of-Graph Neural Networks (G$2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from graphs themselves. Our proposed G$2$GNN outperforms numerous baselines by roughly 5% in both F1-macro and F1-micro scores.
  arXiv  Detail & Related papers  (2021-12-01T02:25:47Z)
• Graph Contrastive Learning with Augmentations [109.23158429991298]
  We propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We show that our framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-22T20:13:43Z)
• GraphCrop: Subgraph Cropping for Graph Classification [36.33477716380905]
  We develop the textbfGraphCrop (Subgraph Cropping) data augmentation method to simulate the real-world noise of sub-structure omission. By preserving the valid structure contexts for graph classification, we encourage GNNs to understand the content of graph structures in a global sense.
  arXiv  Detail & Related papers  (2020-09-22T14:05:41Z)
• Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning [63.97983530843762]
  Graph Neural Networks (GNNs) have achieved great success in graph representation learning. GNNs generate identical representations for graph substructures that may in fact be very different. More powerful GNNs, proposed recently by mimicking higher-order tests, are inefficient as they cannot sparsity of underlying graph structure. We propose Distance Depiction (DE) as a new class of graph representation learning.
  arXiv  Detail & Related papers  (2020-08-31T23:15:40Z)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
  Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information. GNNs are mostly treated as black-boxes and lack human intelligible explanations. We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
  arXiv  Detail & Related papers  (2020-06-03T23:52:43Z)
• Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures [14.932318540666545]
  We study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples. We propose an approach where a probability measure is assigned to each graph based on the spectrum of the graphs normalized Laplacian.
  arXiv  Detail & Related papers  (2020-02-27T17:11:14Z)

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.