SOTAVerified

Node Classification

Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1550 of 1860 papers

TitleStatusHype
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs—0
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining—0
Large-Scale Network Embedding in Apache Spark—0
Latent Network Embedding via Adversarial Auto-encoders—0
Layer-stacked Attention for Heterogeneous Network Embedding—0
Learning Adaptive Neighborhoods for Graph Neural Networks—0
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network—0
Learning Combinatorial Solver for Graph Matching—0
Learning Deep Matrix Representations—0
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks—0
Learning Graph Neural Networks with Positive and Unlabeled Nodes—0
Learning Graph Representations—0
Learning Heuristics for the Maximum Clique Enumeration Problem Using Low Dimensional Representations—0
Learning Node Representations from Noisy Graph Structures—0
Learning on Graphs under Label Noise—0
Optimal Propagation for Graph Neural Networks—0
Learning Robust Representations with Graph Denoising Policy Network—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Learning Spatial-Temporal Graphs for Active Speaker Detection—0
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks—0
Learning to Control the Smoothness of Graph Convolutional Network Features—0
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity—0
Leave Graphs Alone: Addressing Over-Squashing without Rewiring—0
Leiden-Fusion Partitioning Method for Effective Distributed Training of Graph Embeddings—0
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
Leveraging Discourse Structure for Extractive Meeting Summarization—0
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts—0
LFGCN: Levitating over Graphs with Levy Flights—0
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs—0
Linkless Link Prediction via Relational Distillation—0
Link Prediction with Physics-Inspired Graph Neural Networks—0
Link Prediction via Graph Attention Network—0
Little Ball of Fur: A Python Library for Graph Sampling—0
Localized Randomized Smoothing for Collective Robustness Certification—0
Local Structure-aware Graph Contrastive Representation Learning—0
Low-Rank Graph Contrastive Learning for Node Classification—0
Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach—0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns—0
Matrix Manifold Neural Networks++—0
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View—0
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding—0
Memory Augmented Design of Graph Neural Networks—0
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning—0
Message Passing Neural Networks for Hypergraphs—0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
Meta-Inductive Node Classification across Graphs—0
Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous Information Networks—0
Meta-path Free Semi-supervised Learning for Heterogeneous Networks—0
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning—0
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All datasetsCiteseerPubmedCoraCiteSeer with Public Split: fixed 20 nodes per classPubMed with Public Split: fixed 20 nodes per classCora with Public Split: fixed 20 nodes per classPPICoauthor CSCora (0.5%)Cora (1%)Cora (3%)PubMed (0.03%)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy80.09—Unverified
2SplineCNNAccuracy79.2—Unverified
3PathNetAccuracy (%)77.98—Unverified
43ferenceAccuracy76.33—Unverified
5MMAAccuracy76.3—Unverified
6PPNPAccuracy75.83—Unverified
7CoLinkDistAccuracy75.79—Unverified
8CoLinkDistMLPAccuracy75.77—Unverified
9APPNPAccuracy75.73—Unverified
10CleoraAccuracy75.7—Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21—Unverified
2CoLinkDistAccuracy89.58—Unverified
3CoLinkDistMLPAccuracy89.53—Unverified
4PathNetAccuracy (%)88.92—Unverified
53ferenceAccuracy88.9—Unverified
6SplineCNNAccuracy88.88—Unverified
7LinkDistAccuracy88.86—Unverified
8LinkDistMLPAccuracy88.79—Unverified
9PairEF188.57—Unverified
10GCN + MixupAccuracy87.9—Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24—Unverified
2CoLinkDistAccuracy87.89—Unverified
33ferenceAccuracy87.78—Unverified
4LinkDistMLPAccuracy87.58—Unverified
5CoLinkDistMLPAccuracy87.54—Unverified
6CleoraAccuracy86.8—Unverified
7NodeNetAccuracy86.8—Unverified
8MMAAccuracy85.8—Unverified
9GResNet(GAT)Accuracy85.5—Unverified
10TransGNN1:1 Accuracy85.1—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5—Unverified
2LDS-GNNAccuracy75—Unverified
3CPF-tra-APPNPAccuracy74.6—Unverified
4G3NNAccuracy74.5—Unverified
5GEMAccuracy74.2—Unverified
6GGCMAccuracy74.2—Unverified
7Truncated KrylovAccuracy73.86—Unverified
8SSGCAccuracy73.6—Unverified
9OKDEEMAccuracy73.53—Unverified
10GCNIIAccuracy73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4—Unverified
2CPF-tra-GCNIIAccuracy83.2—Unverified
3DSGCNAccuracy81.9—Unverified
4Truncated KrylovAccuracy81.7—Unverified
5SuperGAT MXAccuracy81.7—Unverified
6G-APPNPAccuracy80.95—Unverified
7GGCMAccuracy80.8—Unverified
8GCN(predicted-targets)Accuracy80.42—Unverified
9SSGCAccuracy80.4—Unverified
10GCNIIAccuracy80.2—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9—Unverified
2GCN-TVAccuracy86.3—Unverified
3GCNIIAccuracy85.5—Unverified
4CPF-ind-APPNPAccuracy85.3—Unverified
5AIR-GCNAccuracy84.7—Unverified
6H-GCNAccuracy84.5—Unverified
7G-APPNPAccuracy84.31—Unverified
8SuperGAT MXAccuracy84.3—Unverified
9DSGCNAccuracy84.2—Unverified
10LDS-GNNAccuracy84.1—Unverified