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 1–10 of 982 papers

TitleStatusHype
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs—0
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning—0
Heterogeneous Temporal Hypergraph Neural Network—0
Wasserstein Hypergraph Neural Network—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning—0
Positional Encoding meets Persistent Homology on GraphsCode0
Graph Persistence goes Spectral—0
Studying and Improving Graph Neural Network-based Motif Estimation—0
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
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Benchmark Results

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