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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 626–650 of 927 papers

TitleStatusHype
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Geometrically Principled Connections in Graph Neural Networks—0
Geometric Random Walk Graph Neural Networks via Implicit Layers—0
Geometric Scattering for Graph Data Analysis—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features—0
G-Mixup: Graph Augmentation for Graph Classification—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
Going beyond persistent homology using persistent homology—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Gradient Inversion Attack on Graph Neural Networks—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Graph2Graph Learning with Conditional Autoregressive Models—0
Graph Adversarial Self-Supervised Learning—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction—0
Graph-Aware Transformer: Is Attention All Graphs Need?—0
Graph-based Argument Quality Assessment—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation—0
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All datasetsPROTEINSMUTAGNCI1D&DENZYMESIMDb-BCOLLABPTCIMDb-MNCI109CIFAR10 100kREDDIT-B

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GIN-0Accuracy762—Unverified
2HGP-SLAccuracy84.91—Unverified
3rLap (unsupervised)Accuracy84.3—Unverified
4TFGW ADJ (L=2)Accuracy82.9—Unverified
5FIT-GNNAccuracy82.1—Unverified
6DUGNNAccuracy81.7—Unverified
7MEWISPoolAccuracy80.71—Unverified
8CIN++Accuracy80.5—Unverified
9MAGPoolAccuracy80.36—Unverified
10SAEPoolAccuracy80.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Evolution of Graph ClassifiersAccuracy100—Unverified
2MEWISPoolAccuracy96.66—Unverified
3TFGW ADJ (L=2)Accuracy96.4—Unverified
4GIUNetAccuracy95.7—Unverified
5G_InceptionAccuracy95—Unverified
6GICAccuracy94.44—Unverified
7CIN++Accuracy94.4—Unverified
8sGINAccuracy94.14—Unverified
9CANAccuracy94.1—Unverified
10Deep WL SGN(0,1,2)Accuracy93.68—Unverified
#ModelMetricClaimedVerifiedStatus
1TFGW ADJ (L=2)Accuracy88.1—Unverified
2WKPI-kmeansAccuracy87.2—Unverified
3FGW wl h=4 spAccuracy86.42—Unverified
4WL-OA KernelAccuracy86.1—Unverified
5WL-OAAccuracy86.1—Unverified
6FGW wl h=2 spAccuracy85.82—Unverified
7WWLAccuracy85.75—Unverified
8DUGNNAccuracy85.5—Unverified
9δ-2-LWLAccuracy85.5—Unverified
10CIN++Accuracy85.3—Unverified