Minh-Phu Vuong

Ph.D. candidate at TXST
cty13@txstate.edu | CV | | |

I am a Ph.D. candidate in the Department of Computer Science at Texas State Universtiy, advised by Dr. Chul-Ho Lee. My research centers on graph algorithms, spectral methods, and scalable optimization with applications to networking and machine learning.

Prior to my Ph.D. studies, I obtained my Master’s degree in Computer Science at Jeonbuk National University, South Korea, under the supervision of Dr. Sungchan Kim. I received my Bachelor’s degree in Electrical and Electronics Engineering from Ho Chi Minh City University of Technology (HCMUT), Vietnam, where I was advised by Dr. Ha Hoang Kha.

News

    11/28/25: Our paper “Efficient Monte Carlo Algorithms for Approximating Katz Centrality on Large Graphs” has been accepted at WSDM 2026, Boise, ID, USA.
    10/23/25: Our paper “SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks” has been accepted at IEEE BigData 2025, Macau SAR, China.
    10/06/25: I presented our paper “Effective Delayed Patching for Transient Malware Control on Networks” at IEEE MASS 2025 in Chicago, IL! [slides]
    08/05/25: Our paper “FairAD: Computationally Efficient Fair Graph Clustering via Algebraic Distance” has been accepted at CIKM 2025, Seoul, South Korea.
    07/15/25: Our paper “Effective Delayed Patching for Transient Malware Control on Networks” has been accepted at IEEE MASS 2025, Chicago, IL, USA.

Projects

FairAD

Computationally Efficient Fair Graph Clustering via Algebraic Distance.
FairAD framework illustration
FairAD introduces a scalable framework for fair graph clustering by embedding fairness constraints into an affinity matrix using algebraic distance. Through graph coarsening and a constrained minimization step, it achieves balanced clusters across demographic groups while running up to 40× faster than state-of-the-art fair clustering algorithms.
Effective Delayed Patching for Transient Malware Control on Networks.
Delayed patching framework illustration
This work develops a patching policy that accounts for real-world patching delays, often overlooked in prior studies. By modeling infection dynamics under the SI epidemic model and introducing the notion of “critical edges,” the method formulates a constrained normalized cut problem to select nodes for vaccination under limited resources, significantly outperforming baseline policies in controlling malware spread.

Publications

FairAD: Computationally Efficient Fair Graph Clustering via Algebraic Distance [paper] [slides]
Minh Phu Vuong,Young-Ju Lee, Iván Ojeda-Ruiz and Chul-Ho Lee
The 34th ACM International Conference on Information and Knowledge Management (CIKM 2025)

Effective Delayed Patching for Transient Malware Control on Networks [paper] [slides]
Minh Phu Vuong, Chul-Ho Lee, and Do Young Eun
The 22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2025)

Efficient Monte Carlo Algorithms for Approximating Katz Centrality on Large Graphs
Garrett Cornett, Minh Phu Vuong,and Chul-Ho Lee
The 19th ACM International Conference on Web Search and Data Mining (WSDM 2026)

SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
Xin Huang, Weipeng Zhuo, Minh Phu Vuong, Shiju Li, Jongryool Kim, Bradley Rees and Chul-Ho Lee
The 2025 IEEE International Conference on Big Data (IEEE BigData 2025)

CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks
Xin Huang, Weipeng Zhuo, Minh Phu Vuong, Shiju Li, Jongryool Kim, Bradley Rees and Chul-Ho Lee
https://arxiv.org/abs/2404.02300

Synthesizing Challenging Pose Images for 2D Human Pose Estimation
Minh Phu Vuong, Dohun Lim, Hyeonseok Lee, and Sungchan Kim
Korea Computer Congress 2022

Awards

  • IEEE MASS 2025 Student Travel Grant — U.S. National Science Foundation
  • Doctoral Merit Fellowship — Texas State University
  • Computer Science Research Excellence Award — Texas State University
  • Graduate Research Assistant Tuition Scholarship — Texas State University; Jeonbuk National University