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 101–125 of 982 papers

TitleStatusHype
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
Expander Graph PropagationCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation LearningCode1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Periodic Graph Transformers for Crystal Material Property PredictionCode1
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text ClassificationCode1
Structure-Preserving Graph Representation LearningCode1
Relational Self-Supervised Learning on GraphsCode1
Modeling Two-Way Selection Preference for Person-Job FitCode1
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
Motif-based Graph Representation Learning with Application to Chemical MoleculesCode1
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
A Representation Learning Framework for Property GraphsCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Taxonomy of Benchmarks in Graph Representation LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Metric Based Few-Shot Graph ClassificationCode1
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group DiscriminationCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
Relphormer: Relational Graph Transformer for Knowledge Graph RepresentationsCode1
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Benchmark Results

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