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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

SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning

Strengthening structural baselines for graph classification using Local Topological Profile

Deep Graph Reprogramming

TGNN: A Joint Semi-supervised Framework for Graph-level Classification

ID-MixGCL: Identity Mixup for Graph Contrastive Learning

Stochastic Subgraph Neighborhood Pooling for Subgraph Classification

Transformer and Snowball Graph Convolution Learning for Brain functional network Classification

Topological Pooling on Graphs

Structural Imbalance Aware Graph Augmentation Learning

A Comparison of Graph Neural Networks for Malware Classification

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms

Exphormer: Sparse Transformers for Graphs

MPool: Motif-Based Graph Pooling

Graph Positional Encoding via Random Feature Propagation

AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks

Diffusing Graph Attention

A semantic backdoor attack against Graph Convolutional Networks

Masked Relation Learning for DeepFake Detection

SGL-PT: A Strong Graph Learner with Graph Prompt Tuning

On the Expressivity of Persistent Homology in Graph Learning

Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures

Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification

From Graph Generation to Graph Classification

Energy Transformer

Unnoticeable Backdoor Attacks on Graph Neural Networks

Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search

Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network

Structural Explanations for Graph Neural Networks using HSIC

Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data

Simplifying Subgraph Representation Learning for Scalable Link Prediction

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration

On the Connection Between MPNN and Graph Transformer

DBGDGM: Dynamic Brain Graph Deep Generative Model

Efficiently predicting high resolution mass spectra with graph neural networks

Graph Scattering beyond Wavelet Shackles

Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer