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 626–650 of 982 papers

TitleStatusHype
Graph sampling for node embedding—0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
Improving Graph-Based Text Representations with Character and Word Level N-grams—0
Towards Real-Time Temporal Graph LearningCode0
Uplifting Message Passing Neural Network with Graph Original Information—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
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
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Revisiting Embeddings for Graph Neural Networks—0
Cell Attention NetworksCode0
Machine Learning Partners in Criminal Networks—0
Temporal knowledge graph representation learning with local and global evolutionsCode0
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
Robust Causal Graph Representation Learning against Confounding EffectsCode0
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
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Benchmark Results

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