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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

PlanE: Representation Learning over Planar Graphs

Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification

Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks

An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics

Modeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite Matrices

On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

Structure-Aware Robustness Certificates for Graph Classification

Globally Interpretable Graph Learning via Distribution Matching

Structure-Sensitive Graph Dictionary Embedding for Graph Classification

A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening

Explainable and Position-Aware Learning in Digital Pathology

Self-supervised Learning and Graph Classification under Heterophily

Analysis and Approximate Inference of Large Random Kronecker Graphs

Graph Mixup with Soft Alignments

Path Neural Networks: Expressive and Accurate Graph Neural Networks

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

XInsight: Revealing Model Insights for GNNs with Flow-based Explanations

CIN++: Enhancing Topological Message Passing

Graph Classification Gaussian Processes via Spectral Features

Randomized Schur Complement Views for Graph Contrastive Learning

Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance

Explaining and Adapting Graph Conditional Shift

Message-passing selection: Towards interpretable GNNs for graph classification

EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost

Towards Semi-supervised Universal Graph Classification

Is Rewiring Actually Helpful in Graph Neural Networks?

GIMM: InfoMin-Max for Automated Graph Contrastive Learning

Graph Inductive Biases in Transformers without Message Passing

Robust Ante-hoc Graph Explainer using Bilevel Optimization

Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies

What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding

Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective

Sequential Graph Neural Networks for Source Code Vulnerability Identification

DEGREE: Decomposition Based Explanation For Graph Neural Networks

Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks

DRew: Dynamically Rewired Message Passing with Delay