David Yoon Suk Kang

Orcid: 0000-0002-5892-2265

Affiliations:
  • Chungbuk National University, Cheonju, Korea
  • University of Michigan, School of Information, MI, USA (2022-2024)
  • Hanyang University, Department of Computer and Software, Seoul, Korea (PhD 2022)


According to our database1, David Yoon Suk Kang authored at least 14 papers between 2016 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Anchored Alignment: Preventing Positional Collapse in Multimodal Recommender Systems.
CoRR, March, 2026

Tri-UNetX: Tri-plane UNet with xLSTM for 3D cell segmentation.
Image Vis. Comput., 2026

Revisiting Clique and Star Expansions in Hypergraph Representation Learning: Observations, Problems, and Solutions.
IEEE Access, 2026

Improving the Accuracy of Community Detection on Signed Networks via Community Refinement and Contrastive Learning.
Proceedings of the ACM Web Conference 2026, 2026

2024
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding.
ACM Trans. Knowl. Discov. Data, November, 2024

Low Mileage, High Fidelity: Evaluating Hypergraph Expansion Methods by Quantifying the Information Loss.
Proceedings of the ACM on Web Conference 2024, 2024

2023
A Framework for Accurate Community Detection on Signed Networks Using Adversarial Learning.
IEEE Trans. Knowl. Data Eng., November, 2023

2022
Community reinforcement: An effective and efficient preprocessing method for accurate community detection.
Knowl. Based Syst., 2022

2021
${\sf FORESEE}$FORESEE: An Effective and Efficient Framework for Estimating the Execution Times of IO Traces on the SSD.
IEEE Trans. Computers, 2021

Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks.
Proceedings of the IEEE International Conference on Data Mining, 2021

2020
CR-Graph: Community Reinforcement for Accurate Community Detection.
Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2017
A Framework for Estimating Execution Times of IO Traces on SSDs.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

2016
The uFLIP benchmark revisited for evaluating SSDs.
Int. J. Commun. Syst., 2016

A methodology for estimating execution times of IO traces in SSDs: student research abstract.
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016


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