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 151–160 of 982 papers

TitleStatusHype
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
PolyFormer: Scalable Node-wise Filters via Polynomial Graph TransformerCode0
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
Unsupervised Graph Representation Learning with Inductive Shallow Node EmbeddingCode0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
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
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Benchmark Results

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