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 781–790 of 982 papers

TitleStatusHype
Hyperbolic Neural NetworksCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation LearningCode0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
Graph Representation Learning for Contention and Interference Management in Wireless NetworksCode0
Graph Representation Learning Beyond Node and HomophilyCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
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Benchmark Results

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