What's New
- March, 2022 - We post our recent work, "Zero-shot Domain Adaptation on Heterogeneous Graphs", collaborated with Google Research on Arxiv.
- March, 2022 - We release PASS's source code to the public.
- February, 2022 - I gave a guest lecture on GNNs to the CMU 10707 deep learning class. You can find the record here.
- January, 2022 - I have been selected again as a 2022 Amazon Graduate Research Fellow.
- 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.
- January, 2021 - I have been selected as a 2021 Amazon Graduate Research Fellow.
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 research interests are in the area of Deep Graph Learning.
More specifically, I’m interested in Automation and Democratization of Graph Learning using various deep learning techniques.
My recent work includes automation in 1) polishing/generation of graph structures, 2) generation of node/edge/subgraph representations, and 3) solution generation in the application level.
Ultimately, I’d like to empower all users to benefit from their own graph structures, regardless of their level of expertise in the graph learning field.
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.
Publication
- Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks
Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi
[Arxiv] - 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
- Guest Lecture: Introduction to Graph Neural Networks
Invited as a guest lecturer to "10707: Introduction to Deep Learning" from CMU
[Slide] [Video] - CMU 10707: Introduction to Deep Learning
TA in Spring 2022 - CMU 15780: Graduate Artificial Intelligence
TA in Spring 2021
AWARDS & HONORS
- AWS Cloud Credit for Research,
AWS (Sep. 2021)
Awarding $19,000 AWS Cloud Credit for Research; my project "Automation and Democratization of Graph Mining" was part of the proposal. - Amazon Graduate Research Fellowship, Amazon (Sep. 2021 - Aug. 2022)
Awarding the amount of $70,000 to support scientific research of Masters/PhD students. - 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)
