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 521–530 of 982 papers

TitleStatusHype
Learning to Hash with Graph Neural Networks for Recommender Systems—0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Learning with Capsules: A Survey—0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning—0
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks—0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
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Benchmark Results

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