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 101–110 of 982 papers

TitleStatusHype
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN—0
Sparse Decomposition of Graph Neural Networks—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
Towards Fair Graph Representation Learning in Social Networks—0
Querying functional and structural niches on spatial transcriptomics dataCode0
Information propagation dynamics in Deep Graph Networks—0
A Benchmark on Directed Graph Representation Learning in Hardware Designs—0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
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Benchmark Results

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