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 371–380 of 982 papers

TitleStatusHype
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
Unsupervised Graph Representation Learning with Inductive Shallow Node EmbeddingCode0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
Differential Encoding for Improved Representation Learning over Graphs—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
RobGC: Towards Robust Graph Condensation—0
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Benchmark Results

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