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About

I am currently a PhD student at the Computer Science Department at Carnegie Mellon University, where I am advised by Prof. Christos Faloutsos and Prof. Ruslan Salakhutdinov. My PhD is supported by the Amazon Graduate Research Fellowship and the Kwanjeong Educational Foundation Scholarship. I was a research intern at Amazon.com, LinkedIn, and Google Research. Before joining CMU, I received my bachelor’s and master’s degrees from Seoul National University, South Korea.

Research summary: My research interests are in the area of Deep Learning on Graphs (DLG). More specifically, I’m interested in making DLG more practical by narrowing gaps between academia and industry. I analyze conventional problem settings in the DLG pipeline and propose new directions to make them more generalizable and applicable to real-world settings.

Research opportunities: I am usually looking for students to help with research projects both during the semester and over the summer. If you are interested, please send me an email. I especially encourage students from underrepresented groups to reach out.

Publication

  • Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks
    Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov
    [Arxiv]
  • Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks
    Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi
    Neural Information Processing Systems (NeurIPS) 2022
    [Arxiv]
  • A Dataset on Malicious Paper Bidding in Peer Review
    Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang
    [Arxiv]
  • Autonomous Graph Mining Algorithm Search with Best Performance Trade-off
    Minji Yoon, Theophile Gervet, Bryan Hooi, and Christos Faloutsos
    SCIE Journal, Knowledge and Information Systems (KAIS) 2022
    [PDF] [shorter ver.] [Code] [Slide]
  • Graph Fraud Detection Based on Accessibility Score Distributions
    Minji Yoon
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2021
    [PDF] [Code] [Slide]
  • Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks
    Minji Yoon, Theophile Gervet, Baoxu Shi, Sufeng Niu, Qi He, Jaewon Yang
    SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2021
    [PDF] [Slide] [Video] [Code] [CMU blog] [LinkedIn blog]
  • Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
    Minji Yoon, Theophile Gervet, Bryan Hooi, and Christos Faloutsos
    20th IEEE International Conference on Data Mining (ICDM) 2020
    (Acceptance Ratio: 9.8%)
    **Selected as one of the best papers of ICDM’20 for a fast track journal invitation at KAIS
    [PDF] [Code] [Slide]
  • Provably Robust Node Classification via Low-Pass Message Passing
    Yiwei Wang, Shenghua Liu, Minji Yoon, Hemank Lamba, Wei Wang, Christos Faloutsos, and Bryan Hooi
    20th IEEE International Conference on Data Mining (ICDM) 2020
    (Acceptance Ratio: 9.8%)
    [PDF]
  • MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
    Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos
    34th AAAI Conference on Artificial Intelligence (AAAI) 2020
    [PDF]
  • Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
    Minji Yoon, Bryan Hooi, Kijung Shin, and Christos Faloutsos
    SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019, Alaska, USA
    [PDF(old)] [PDF(updated)] [Code] [Poster]
  • Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees
    Minji Yoon, Woojeong Jin, and U Kang
    The Web Conference (WWW) 2018, Lyon, France
    (Acceptance Ratio: 14.8%)
    [PDF] [Code] [Slide]
  • TPA: Fast, Scalable and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs
    Minji Yoon, Jinhong Jung, and U Kang
    IEEE International Conference on Data Engineering (ICDE) 2018, Paris, France
    [PDF] [Code] [Slide]

Work Experiences

  • Graph Mining team, Google Research
    Research Intern (May. 2021 - Aug. 2021)
    Transfer learning between different node types on a heterogeneous graph
  • Standardization team, LinkedIn
    Machine Learning Engineer Intern (May. 2020 - Aug. 2020)
    Developed an algorithm to optimize computation graphs in Graph Neural Networks (GNNs)
  • CTPS Machine Learning Accelation team, Amazon.com
    Applied Scientist Intern (May. 2019 - Aug. 2019)
    Developed a fast and scalable algorithm for fraud detection in Amazon.com
  • Data Mining Lab, Seoul National University
    Research Intern (Apr. 2017 - Jun. 2018)
    Developed fast, accurate and scalable algorithms for Random Walk with Restart (RWR)
  • Session team, SAP Labs Korea
    Software Developer (Sep. 2014 - Mar. 2017)
    Developed an in-memory database SAP HANA

Teaching Experiences

AWARDS & HONORS

  • Amazon Graduate Research Fellowship, Amazon (Sep. 2021 - Aug. 2023)
    Awarding the amount of $70,000 to support scientific research of graduate students per year.
  • AWS Cloud Credit for Research, AWS (Sep. 2021 - Aug. 2022)
    Awarding $19,000 AWS Cloud Credit for Research; my project "Automation and Democratization of Graph Mining" was part of the proposal.
  • Kwanjeong Educational Foundation Scholarship, Kwanjeong Foundation (Sep. 2018 - Aug. 2022)
    4 years for Doctor’s Degree.
  • National Science & Technology Scholarship, KOSAF (Mar. 2008 - Feb. 2012)
    Full tuition exemptions for 8 semesters.
  • Cum Laude Graduation Honors, Seoul National University (Feb. 2012)