SOTAVerified

Graph Classification

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.

( Image credit: Hierarchical Graph Pooling with Structure Learning )

Papers

Showing 651700 of 927 papers

TitleStatusHype
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts0
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective0
Towards Semi-supervised Universal Graph Classification0
Towards Subgraph Isomorphism Counting with Graph Kernels0
Transductive Spiking Graph Neural Networks for Loihi0
Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification0
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks0
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning0
Understanding the Power of Persistence Pairing via Permutation Test0
Understanding the Power of Persistence Pairing via Permutation Test0
Unified Graph Structured Models for Video Understanding0
Universal Graph Continual Learning0
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data0
Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes0
Unsupervised Universal Self-Attention Network for Graph Classification0
Walk-Steered Convolution for Graph Classification0
Wasserstein-based Graph Alignment0
Wasserstein Weisfeiler-Lehman Subtree Distance for Graph-Structured Data0
Watermarking Graph Neural Networks based on Backdoor Attacks0
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer0
Weakly Supervised Learning on Large Graphs0
Fundamental Limits in Formal Verification of Message-Passing Neural Networks0
Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs0
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations0
When Work Matters: Transforming Classical Network Structures to Graph CNN0
XInsight: Revealing Model Insights for GNNs with Flow-based Explanations0
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis0
Efficient Mixed Precision Quantization in Graph Neural NetworksCode0
Structural Entropy Guided Unsupervised Graph Out-Of-Distribution DetectionCode0
Efficient Automatic Machine Learning via Design GraphsCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
edGNN: a Simple and Powerful GNN for Directed Labeled GraphsCode0
Edge Classification on Graphs: New Directions in Topological ImbalanceCode0
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed GraphsCode0
Generalized Equivariance and Preferential Labeling for GNN Node ClassificationCode0
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on GraphsCode0
DOTIN: Dropping Task-Irrelevant Nodes for GNNsCode0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Distance Metric Learning for Graph Structured DataCode0
Permute Me Softly: Learning Soft Permutations for Graph RepresentationsCode0
Persistence Images: A Stable Vector Representation of Persistent HomologyCode0
PiNet: A Permutation Invariant Graph Neural Network for Graph ClassificationCode0
PiNet: Attention Pooling for Graph ClassificationCode0
TreeRNN: Topology-Preserving Deep GraphEmbedding and LearningCode0
Structural Optimization Makes Graph Classification Simpler and BetterCode0
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph ClassificationCode0
Structure-Aware Hierarchical Graph Pooling using Information BottleneckCode0
Pooling Strategies for Simplicial Convolutional NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762Unverified
2HGP-SLAccuracy84.91Unverified
3rLap (unsupervised)Accuracy84.3Unverified
4TFGW ADJ (L=2)Accuracy82.9Unverified
5FIT-GNNAccuracy82.1Unverified
6DUGNNAccuracy81.7Unverified
7MEWISPoolAccuracy80.71Unverified
8CIN++Accuracy80.5Unverified
9MAGPoolAccuracy80.36Unverified
10SAEPoolAccuracy80.36Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100Unverified
2MEWISPoolAccuracy96.66Unverified
3TFGW ADJ (L=2)Accuracy96.4Unverified
4GIUNetAccuracy95.7Unverified
5G_InceptionAccuracy95Unverified
6GICAccuracy94.44Unverified
7CIN++Accuracy94.4Unverified
8sGINAccuracy94.14Unverified
9CANAccuracy94.1Unverified
10Deep WL SGN(0,1,2)Accuracy93.68Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1Unverified
2WKPI-kmeansAccuracy87.2Unverified
3FGW wl h=4 spAccuracy86.42Unverified
4WL-OA KernelAccuracy86.1Unverified
5WL-OAAccuracy86.1Unverified
6FGW wl h=2 spAccuracy85.82Unverified
7WWLAccuracy85.75Unverified
8δ-2-LWLAccuracy85.5Unverified
9DUGNNAccuracy85.5Unverified
10CIN++Accuracy85.3Unverified