What's New
- May, 2023 - Our work collaborated with Google Research, "Graph Generative Model for Benchmarking Graph Neural Networks" got accected to ICML 2023.
- March, 2023 - Google introduced my intern project, KTN (Neurips'22), in their official blog.
- January, 2023 - I was selected as a 2023 Amazon Graduate Research Fellow. Three years in a row!
- October, 2022 - I did my thesis proposal titled "Toward more practical Deep Learning on Graphs".
- September, 2022 - Our work collaborated with Google Research, "Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks" got accected to NeurIPS 2022.
- March, 2022 - LinkedIn releases our collaborated work PASS's source code to the public.
- February, 2022 - I gave a guest lecture on GNNs to the CMU 10707: Advanced Deep Learning class. You can find the record here.
- September, 2021 - We won $19,000 AWS Cloud Credits for Research; my project "Automation and Democratization of Graph Mining" was part of the proposal.
- September, 2021 - I started being co-advised by Prof. Ruslan Salakhutdinov.
About
I am currently a 5th year 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, 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 across four dimensions:
- Automation: automate algorithm search and hyperparameter tuning under the message-passing framework (AutoGM)
- Generalization: enable existing DLG algorithms specialized to molecule graphs to generalize to diverse types of graphs
- Scalability: sample neighbors to regulate the computation cost while filtering out noisy neighbors adaptively for a task (PASS)
- privacy: redefine conventional problem definitions, including graph generation and transfer learning, to be aware of the privacy-restricted nature of real-world graphs (KTN, CGT)
Publication
- Graph Generative Model for Benchmarking Graph Neural Networks
Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov
International Conference on Machine Learning (ICML) 2023
[Paper] - A Dataset on Malicious Paper Bidding in Peer Review
Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang
The Web Conference 2023
[Paper] [Arxiv] - Toward more Practical Deep Learning on Graphs
Minji Yoon, Thesis Proposal 2022
Thesis Committee: Christos Faloutsos (CMU), Ruslan Salakhutdinov (CMU), Tom M. Mitchell (CMU), Jure Leskovec (Stanford university)
- 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
[Paper] [Arxiv] [Code] [Slide] [DLG workshop 2022] [Google AI blog] - Autonomous Graph Mining Algorithm Search with Best Performance Trade-off
Minji Yoon, Theophile Gervet, Bryan Hooi, Christos Faloutsos
SCIE Journal, Knowledge and Information Systems (KAIS) 2022
[Paper] [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
[Paper] [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
[Paper] [Slide] [Video] [Code] [CMU blog] [LinkedIn blog] [VentureBeat] [MarketsInsider] - Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off
Minji Yoon, Theophile Gervet, Bryan Hooi, 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
[Paper] [Code] [Slide] - Provably Robust Node Classification via Low-Pass Message Passing
Yiwei Wang, Shenghua Liu, Minji Yoon, Hemank Lamba, Wei Wang, Christos Faloutsos, Bryan Hooi
20th IEEE International Conference on Data Mining (ICDM) 2020
(Acceptance Ratio: 9.8%)
[Paper] - MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, Christos Faloutsos
34th AAAI Conference on Artificial Intelligence (AAAI) 2020
[Paper] - Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach
Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019, Alaska, USA
[Paper(updated)] [Code] [Poster] - Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees
Minji Yoon, Woojeong Jin, U Kang
The Web Conference (WWW) 2018, Lyon, France
(Acceptance Ratio: 14.8%)
[Paper] [Code] [Slide] - TPA: Fast, Scalable and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs
Minji Yoon, Jinhong Jung, U Kang
IEEE International Conference on Data Engineering (ICDE) 2018, Paris, France
[Paper] [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
- Guest Lecture: Introduction to Graph Neural Networks
Delievered one lecture to CMU 10417/10617: Intermediate Deep Learning class and CMU 10707: Advanced Deep Learning class, respectively
[Slide] [Video] - CMU 10707: Introduction to Deep Learning
Lectured by Ruslan Salakhutdinov
Teaching Assistant, Spring 2022 - CMU 15780: Graduate Artificial Intelligence
Lectured by Stephanie Rosenthal, Nihar B. Shah
Teaching Assistant, Spring 2021
AWARDS & HONORS
- Amazon Graduate Research Fellowship, Amazon (Sep. 2020 - 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)
Student Advising
Some amazing students I've had the pleasure of advising:- Selvan Sunitha Ravi (master student at LTI, CMU)
- Rohan Kumar (master student at LTI, CMU)
- Youngmin Kim (master student at LTI, CMU)
- Jeffrey Tsaw (master student at ECE, CMU)
