Graph Representation Learning

DOI10.2200/S01045ED1V01Y202009AIM046zbMath1457.68005OpenAlexW4206482253MaRDI QIDQ5126696

William Hamilton

Publication date: 20 October 2020

Published in: Synthesis Lectures on Artificial Intelligence and Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2200/s01045ed1v01y202009aim046

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Research exposition (monographs, survey articles) pertaining to computer science (68-02)

Related Items (9)

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment ⋮ Transferability of graph neural networks: an extended graphon approach ⋮ Deep learning and geometric deep learning: An introduction for mathematicians and physicists ⋮ Entropic Optimal Transport on Random Graphs ⋮ Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders ⋮ A HYBRID MEMBERSHIP LATENT DISTANCE MODEL FOR UNSIGNED AND SIGNED INTEGER WEIGHTED NETWORKS ⋮ Unnamed Item ⋮ On the expressive power of message-passing neural networks as global feature map transformers ⋮ Learning physical properties of anomalous random walks using graph neural networks

Uses Software

• node2vec
• BERT
• XLNet
• PointNet
• GraRep
• MolGAN
• GraphRNN
• struc2vec
• InfoGraph
• RotatE
• CayleyNets
• DeepWalk
• LINE
• CLUTRR
• GMNN
• Walklets
• STransE
• LanczosNet
• NetGAN
• GraphVAE
• NetProbe

Cites Work

• node2vec

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