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 301–325 of 982 papers

TitleStatusHype
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Adaptive Sampling Towards Fast Graph Representation LearningCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
Hyperbolic Neural NetworksCode0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Cycle Representation Learning for Inductive Relation PredictionCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Heterogeneous Deep Graph InfomaxCode0
Adaptive Graph Representation Learning for Video Person Re-identificationCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
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Benchmark Results

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