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 751–760 of 982 papers

TitleStatusHype
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule MiningCode0
Knowledge Graph Representation Learning using Ordinary Differential Equations—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Graph Communal Contrastive LearningCode0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks—0
Tackling the Local Bias in Federated Graph Learning—0
DPGNN: Dual-Perception Graph Neural Network for Representation Learning—0
Residual2Vec: Debiasing graph embedding with random graphsCode0
MGC: A Complex-Valued Graph Convolutional Network for Directed GraphsCode0
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Benchmark Results

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