Unsupervised Graph Representation Learning Beyond Aggregated View

Unsupervised graph representation learning aims to condense graph information into dense vector embeddings to support various downstream tasks. To achieve this goal, existing UGRL approaches mainly adopt the message-passing mechanism to simultaneously incorporate graph topology and node attribute wi...

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Bibliographic Details

Published in:	IEEE transactions on knowledge and data engineering Vol. 36; no. 12; pp. 9504 - 9516
Main Authors:	Zhou, Jian, Li, Jiasheng, Kuang, Li, Gui, Ning
Format:	Journal Article
Language:	English
Published:	IEEE 01.12.2024
Subjects:	Benchmark testing Data mining Decoding dual-view autoencoder Encoding Graph representation learning Network topology self-supervised Synthetic data Topology topology and attribute features
ISSN:	1041-4347, 1558-2191
Online Access:	Get full text
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