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 271–280 of 982 papers

TitleStatusHype
On the Initialization of Graph Neural NetworksCode0
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers—0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
Recurrent Distance Filtering for Graph Representation LearningCode1
Normed Spaces for Graph EmbeddingCode0
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers—0
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
MultiGPrompt for Multi-Task Pre-Training and Prompting on GraphsCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
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Benchmark Results

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