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 551–560 of 982 papers

TitleStatusHype
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation LearningCode0
Taxonomy of Benchmarks in Graph Representation LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Metric Based Few-Shot Graph ClassificationCode1
Learning with Capsules: A Survey—0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
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
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning—0
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Benchmark Results

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