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 626–650 of 982 papers

TitleStatusHype
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
Local2Global: A distributed approach for scaling representation learning on graphsCode0
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection—0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network—0
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
Semi-Supervised Graph Attention Networks for Event Representation LearningCode0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
RepBin: Constraint-based Graph Representation Learning for Metagenomic BinningCode1
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Learning to Model the Relationship Between Brain Structural and Functional ConnectomesCode0
Knowledge-enhanced Session-based Recommendation with Temporal Transformer—0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Graph Representation Learning via Contrasting Cluster Assignments—0
A Self-supervised Mixed-curvature Graph Neural Network—0
Equivariant Quantum Graph Circuits—0
Siamese Attribute-missing Graph Auto-encoder—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Consensus Graph Representation Learning for Better Grounded Image Captioning—0
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Benchmark Results

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