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

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Showing 501–525 of 982 papers

TitleStatusHype
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Graph Self-Contrast Representation Learning—0
RDGSL: Dynamic Graph Representation Learning with Structure LearningCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
RESTORE: Graph Embedding Assessment Through Reconstruction—0
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
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
Local Structure-aware Graph Contrastive Representation Learning—0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
Graph Contrastive Learning with Generative Adversarial Network—0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Frameless Graph Knowledge DistillationCode0
Neural Causal Graph Collaborative FilteringCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
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Benchmark Results

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