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 326–350 of 982 papers

TitleStatusHype
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
RESTORE: Graph Embedding Assessment Through Reconstruction—0
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based SimilarityCode1
Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic NetworksCode2
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Local Structure-aware Graph Contrastive Representation Learning—0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
Graph Contrastive Learning with Generative Adversarial Network—0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
Neural Architecture RetrievalCode1
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Frameless Graph Knowledge DistillationCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Neural Causal Graph Collaborative FilteringCode0
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Benchmark Results

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