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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

Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization

Density-aware Walks for Coordinated Campaign Detection

Positional Encoding meets Persistent Homology on Graphs

Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs

Improving the Effective Receptive Field of Message-Passing Neural Networks

Graph Style Transfer for Counterfactual Explainability

Scalable Graph Generative Modeling via Substructure Sequences

Addressing the Scarcity of Benchmarks for Graph XAI

Schreier-Coset Graph Propagation

Efficient Mixed Precision Quantization in Graph Neural Networks

Rhomboid Tiling for Geometric Graph Deep Learning

Graffe: Graph Representation Learning via Diffusion Probabilistic Models

DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets

Graph Fourier Transformer with Structure-Frequency Information

LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisation

InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data

AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification

Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations

Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks

Network Embedding Exploration Tool (NEExT)

A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks

Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization

Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection

Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing

Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends

Neural Network Graph Similarity Computation Based on Graph Fusion

Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning

Graph Self-Supervised Learning with Learnable Structural and Positional Encodings

Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection

Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin

Classification of Temporal Graphs using Persistent Homology

Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation

Graph Neural Networks at a Fraction