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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

MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification

Kolmogorov-Arnold Graph Neural Networks

KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning

TopoGCL: Topological Graph Contrastive Learning

Next Level Message-Passing with Hierarchical Support Graphs

Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers

On GNN explanability with activation rules

Edge Classification on Graphs: New Directions in Topological Imbalance

Robustness Inspired Graph Backdoor Defense

Motif-driven Subgraph Structure Learning for Graph Classification

A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability

How Interpretable Are Interpretable Graph Neural Networks?

Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective

GENIE: Watermarking Graph Neural Networks for Link Prediction

PANDA: Expanded Width-Aware Message Passing Beyond Rewiring

GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks

Learning Long Range Dependencies on Graphs via Random Walks

GEFL: Extended Filtration Learning for Graph Classification

Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters

Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs

CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge

Spatio-Spectral Graph Neural Networks

Cross-Context Backdoor Attacks against Graph Prompt Learning

A Canonicalization Perspective on Invariant and Equivariant Learning

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning

Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks

ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks

Perception-Inspired Graph Convolution for Music Understanding Tasks

Towards Subgraph Isomorphism Counting with Graph Kernels

Conditional Local Feature Encoding for Graph Neural Networks

Hypergraph-enhanced Dual Semi-supervised Graph Classification

Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks

Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data

Transductive Spiking Graph Neural Networks for Loihi

Gradformer: Graph Transformer with Exponential Decay