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 161–170 of 982 papers

TitleStatusHype
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
RobGC: Towards Robust Graph Condensation—0
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs—0
A Scalable and Effective Alternative to Graph Transformers—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
OLGA: One-cLass Graph AutoencoderCode0
Introducing Diminutive Causal Structure into Graph Representation Learning—0
Learning Long Range Dependencies on Graphs via Random WalksCode1
Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label ClassificationCode0
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Benchmark Results

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