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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

One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion

CKGConv: General Graph Convolution with Continuous Kernels

SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling

A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only

An Efficient Loop and Clique Coarsening Algorithm for Graph Classification

Improving the interpretability of GNN predictions through conformal-based graph sparsification

Graph data augmentation with Gromow-Wasserstein Barycenters

Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing

Learning the mechanisms of network growth

AKBR: Learning Adaptive Kernel-based Representations for Graph Classification

SSHPool: The Separated Subgraph-based Hierarchical Pooling

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Molecular Classification Using Hyperdimensional Graph Classification

Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection

A Differential Geometric View and Explainability of GNN on Evolving Graphs

Cooperative Classification and Rationalization for Graph Generalization

HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning

Multi-Scale Subgraph Contrastive Learning

Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective

Graph Parsing Networks

Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective

Verifying message-passing neural networks via topology-based bounds tightening

CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning

An end-to-end attention-based approach for learning on graphs

Class-Balanced and Reinforced Active Learning on Graphs

Ising on the Graph: Task-specific Graph Subsampling via the Ising Model

SimMLP: Training MLPs on Graphs without Supervision

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning

Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute

Generalization Error of Graph Neural Networks in the Mean-field Regime

Flexible infinite-width graph convolutional networks and the importance of representation learning

Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels

A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer

Topology-Informed Graph Transformer

Towards Neural Scaling Laws on Graphs