Movement Attentive Graph Embedding for Improving Next Point of Access Prediction-Reference-Cited by-同舟云学术

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Movement Attentive Graph Embedding for Improving Next Point of Access Prediction

Published:2024-01-03 Issue: Volume: Page:1-3
ISSN:
Container-title:2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)
language:
Short-container-title:

Author:

Kang Honggu¹,Kim Taesoo²,Yang Huigyu³,Choo Hyunseung¹

Affiliation:

1. Sungkyunkwan University,Dept. of AI Systems Engineering,Suwon,South Korea

2. Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,South Korea

3. Sungkyunkwan University,Dept. of Superintelligence Engineering,Suwon,South Korea

Funder

IITP

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10418251/10418242/10418377.pdf?arnumber=10418377

Reference8 articles.

1. A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)

2. EMP-GAN: Encoder-Decoder Generative Adversarial Network for Mobility Prediction

3. Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead

4. Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns

5. Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing

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