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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

Graph Classification Gaussian Processes via Hodgelet Spectral Features

Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification

Degree-Conscious Spiking Graph for Cross-Domain Adaptation

Vector-ICL: In-context Learning with Continuous Vector Representations

Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models

NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes

Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph

Higher-Order Message Passing for Glycan Representation Learning

PropEnc: A Property Encoder for Graph Neural Networks

MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length

CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs

MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization

GSpect: Spectral Filtering for Cross-Scale Graph Classification

GSTAM: Efficient Graph Distillation with Structural Attention-Matching

Learning Tree-Structured Composition of Data Augmentation

Graph Classification via Reference Distribution Learning: Theory and Practice

Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs

Graph Classification with GNNs: Optimisation, Representation and Inductive Bias

Graph Triple Attention Network: A Decoupled Perspective

A Structural Feature-Based Approach for Comprehensive Graph Classification

Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images

DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization

Knowledge Probing for Graph Representation Learning

Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization

AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning

Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs

Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques

DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning

Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks

HyperAggregation: Aggregating over Graph Edges with Hypernetworks

Relaxing Graph Transformers for Adversarial Attacks

Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach

Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification

Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs

Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning

Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks