Structural Hierarchy-Enhanced Network Representation Learning
Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should be pushed together and away in the embedding spac...
Main Authors: | Cheng-Te Li, Hong-Yu Lin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-10-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/20/7214 |
Similar Items
-
Network Representation Learning With Community Awareness and Its Applications in Brain Networks
by: Min Shi, et al.
Published: (2022-05-01) -
FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings
by: Ahmad Mel, et al.
Published: (2021-10-01) -
A Novel Global Prototype-Based Node Embedding Technique
by: Zyad Alkayem, et al.
Published: (2022-01-01) -
Multi-Task Network Representation Learning
by: Yu Xie, et al.
Published: (2020-01-01) -
Network Representation Learning Guided by Partial Community Structure
by: Hanlin Sun, et al.
Published: (2020-01-01)