Understanding Graph Embedding Methods and Their Applications

DOI10.1137/20M1386062MaRDI QIDQ5162644

Mengjia Xu

Publication date: 5 November 2021

Published in: SIAM Review (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.08019

zbMATH Keywords

similarityintrinsic dimensionhigh-dimensionalityuncertainty quantificationlatent spacedeep neural networksgraph embedding at scale

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Graph theory (including graph drawing) in computer science (68R10) Knowledge representation (68T30) Reasoning under uncertainty in the context of artificial intelligence (68T37) Information theory (general) (94A15) Graph representations (geometric and intersection representations, etc.) (05C62)

Related Items (1)

Community detection based on competitive walking network embedding method

Uses Software

• WordNet
• SNAP
• word2vec
• PyTorch
• node2vec
• Freebase
• GraRep
• DGL
• DeepWalk
• DynGEM
• dyngraph2vec
• LINE

Cites Work

• Unnamed Item

This page was built for publication: Understanding Graph Embedding Methods and Their Applications