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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

A Causal Disentangled Multi-Granularity Graph Classification Method

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning

Self-supervision meets kernel graph neural models: From architecture to augmentations

Mirage: Model-Agnostic Graph Distillation for Graph Classification

Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose

Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation

One for All: Towards Training One Graph Model for All Classification Tasks

Graph-level Representation Learning with Joint-Embedding Predictive Architectures

GDM: Dual Mixup for Graph Classification with Limited Supervision

Recovering Missing Node Features with Local Structure-based Embeddings

Graph Neural Networks Use Graphs When They Shouldn't

Filtration Surfaces for Dynamic Graph Classification

Generalized Simplicial Attention Neural Networks

MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention

Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

Curvature-based Pooling within Graph Neural Networks

Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images

Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN

TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems

Universal Graph Continual Learning

Cached Operator Reordering: A Unified View for Fast GNN Training

Enhancing Graph Transformers with Hierarchical Distance Structural Encoding

Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors

Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity

Modeling Edge Features with Deep Bayesian Graph Networks

S-Mixup: Structural Mixup for Graph Neural Networks

Graph Out-of-Distribution Generalization with Controllable Data Augmentation

Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes

Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing

RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification

Evaluating Link Prediction Explanations for Graph Neural Networks

Counterfactual Explanations for Graph Classification Through the Lenses of Density

Hypergraph Isomorphism Computation

How Curvature Enhance the Adaptation Power of Framelet GCNs

A Survey on Graph Classification and Link Prediction based on GNN