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 201–225 of 982 papers

TitleStatusHype
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discoveryCode1
Edge Representation Learning with HypergraphsCode1
PC-Conv: Unifying Homophily and Heterophily with Two-fold FilteringCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Pre-training Molecular Graph Representation with 3D GeometryCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group DiscriminationCode1
Reward Propagation Using Graph Convolutional NetworksCode1
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
Multi-hop Attention Graph Neural NetworkCode1
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
Disentangle-based Continual Graph Representation LearningCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
MultiGPrompt for Multi-Task Pre-Training and Prompting on GraphsCode1
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Benchmark Results

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