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
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph EmbeddingsCode2
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
Material Prediction for Design Automation Using Graph Representation LearningCode0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
Periodic Graph Transformers for Crystal Material Property PredictionCode1
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
Revisiting Embeddings for Graph Neural Networks—0
Cell Attention NetworksCode0
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text ClassificationCode1
Machine Learning Partners in Criminal Networks—0
Temporal knowledge graph representation learning with local and global evolutionsCode0
Structure-Preserving Graph Representation LearningCode1
A Class-Aware Representation Refinement Framework for Graph Classification—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Relational Self-Supervised Learning on GraphsCode1
Robust Causal Graph Representation Learning against Confounding EffectsCode0
Modeling Two-Way Selection Preference for Person-Job FitCode1
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax—0
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
ARIEL: Adversarial Graph Contrastive LearningCode0
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Benchmark Results

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