SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 982 papers

TitleStatusHype
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Calibrating and Improving Graph Contrastive LearningCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Graph Contrastive Learning for Connectome ClassificationCode0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Hyperbolic Neural NetworksCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community LabelingCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified