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 851–860 of 982 papers

TitleStatusHype
About Graph Degeneracy, Representation Learning and ScalabilityCode0
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network MotifsCode0
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning—0
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
node2coords: Graph Representation Learning with Wasserstein Barycenters—0
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Benchmark Results

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