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Graph Classification

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Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

<span style="color:grey; opacity: 0.6">( Image credit: Hierarchical Graph Pooling with Structure Learning )</span>

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Graph Classification

논문

Beyond Message Passing: Neural Graph Pattern Machine

Molecular Fingerprints Are Strong Models for Peptide Function Prediction

DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach

A Unified Invariant Learning Framework for Graph Classification

Catch Causal Signals from Edges for Label Imbalance in Graph Classification

Weakly Supervised Learning on Large Graphs

Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements

Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning

Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning

Graph Size-imbalanced Learning with Energy-guided Structural Smoothing

Cluster-guided Contrastive Class-imbalanced Graph Classification

Semi-Implicit Neural Ordinary Differential Equations

Robustness of Graph Classification: failure modes, causes, and noise-resistant loss in Graph Neural Networks

Covered Forest: Fine-grained generalization analysis of graph neural networks

Training MLPs on Graphs without Supervision

GQWformer: A Quantum-based Transformer for Graph Representation Learning

Gradient Inversion Attack on Graph Neural Networks

From Graph Diffusion to Graph Classification

GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers

MADE: Graph Backdoor Defense with Masked Unlearning

Conditional Distribution Learning on Graphs

The GECo algorithm for Graph Neural Networks Explanation

Efficient and Robust Continual Graph Learning for Graph Classification in Biology

Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation

Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network

MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings

Towards Dynamic Message Passing on Graphs

Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networks

Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks

RAGraph: A General Retrieval-Augmented Graph Learning Framework

Uncovering Capabilities of Model Pruning in Graph Contrastive Learning

Conditional Prediction ROC Bands for Graph Classification

Conditional Uncertainty Quantification for Tensorized Topological Neural Networks

Tensor-Fused Multi-View Graph Contrastive Learning

FIT-GNN: Faster Inference Time for GNNs Using Coarsening

Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning