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

Showing 931940 of 982 papers

TitleStatusHype
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Conditional Distribution Learning on GraphsCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Multi-Task Graph AutoencodersCode0
Self-supervised Consensus Representation Learning for Attributed GraphCode0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
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Benchmark Results

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