Kijung Shin (신기정)

Ewon Assistant Professor
Data Mining Lab, KAIST AI & EE

About Me

I am an Ewon Assistant Professor in the Graduate School of AI and the School of Electrical Engineering (Computer Division) at KAIST. I received my Ph.D. in Computer Science from Carnegie Mellon University, where I was advised by Prof. Christos Faloutsos and supported by the KFAS Scholarship and the Siebel Scholar Fellowship. I received my B.S. in Computer Science and Engineering and B.A. in Economics from Seoul National University. My research interests include data mining, graph mining, and scalable machine learning.

At KAIST, I lead the Data Mining Lab.

Contact Details

Email: kijungs (at) kaist.ac.kr
Web: https://kijungs.github.io
Address:
Graduate School of AI, KAIST
85, Hoegi-ro, Dongdaemun-gu
Seoul, 02455, Republic of Korea

Download C.V.

Education

Carnegie Mellon University

Ph.D. in Computer Science •Feb. 2019
M.S. in Computer Science •Dec. 2017

Seoul National University

B.S. in Computer Science and Engineering•Aug. 2015
B.A. in Economics (Double Major) •Aug. 2015

Positions

KAIST

Assistant Professor • Feb. 2019 - Present

LinkedIn

Research Intern • May. 2017 - Aug. 2017 & May. 2018 - Aug. 2018

CYRAM

Associate Researcher • Jan. 2011 - Dec. 2013

Teaching

KAIST AI506 Data Mining and Search

Instructor • Spring 2021 [ link ], Spring 2020 [ link ]

KAIST AI607 Graph Mining and Social Network Analysis

Instructor • Fall 2020 [ link ], Fall 2019 [ link ]

KAIST EE210 Probability and Introductory Random Processes

Instructor • Fall 2020 [ link ]

KAIST EE209(B) Programming Structure for Electrical Engineering

Instructor • Spring 2019 [ link ]

CMU 10-601 Introduction to Machine Learning

Teaching Assistant • Fall 2017 [ link ]

CMU 15-780 Graduate Artificial Intelligence

Teaching Assistant • Spring 2017 [ link ]

Publications

[ Google Scholar | DBLP | Research Gate ]


2021 or Later

[C34]
Efficient Neural Network Approximation of Robust PCA for Automated Analysis of
Calcium Imaging Data

Seungjae Han, Eun-Seo Cho, Inkyu Park, Kijung Shin, and Young-Gyu Yoon
MICCAI 2021 [ paper | code | bib ]

[C33]
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams

{Taehyung Kwon, Inkyu Park}, Dongjin Lee, and Kijung Shin
ICDE 2021 [ paper | appendix | video | slides | code and datasets | bib ]

[C32]
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers

Dongjin Lee and Kijung Shin
ICDE 2021 [ paper | appendix | video | slides | code and datasets | bib ]

[C31]
How Do Hyperedges Overlap in Real-World Hypergraphs? - Patterns, Measures, and Generators

{Geon Lee, Minyoung Choe}, and Kijung Shin
WWW 2021 [ paper | appendix | video | slides | code and datasets | bib ]

[C30]
PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation

Minseok Kim, Hwanjun Song, Doyoung Kim, Kijung Shin, and Jae-Gil Lee
AAAI 2021 [ paper | bib ]

[C29]
DPGS: Degree-Preserving Graph Summarization

Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, Huawei Shen, and Xueqi Cheng
SDM 2021 [ paper | code and datasets | bib ]

[J10]
Detecting Group Anomalies in Tera-Scale Multi-Aspect Data via Dense-Subtensor Mining

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
Frontiers in Big Data [ paper | shorter ver. [C8] | code and datasets | bib ]

[J9]
CoCoS: Fast and Accurate Distributed Triangle Counting in Graph Streams

Kijung Shin, Euiwoong Lee, Jinoh Oh, Mohammad Hammoud, and Christos Faloutsos
ACM TKDD [ paper | shorter ver. [C13] | code and datasets | bib ]


2020

[C28]
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks

Jihoon Ko, Kyuhan Lee, Kijung Shin, and Noseong Park
ASONAM 2020 [ paper | slides | code and datasets | bib ]
Selected for fast-track journal invitation

[C27]
Evolution of Real-world Hypergraphs: Patterns and Models without Oracles

Yunbum Kook, Jihoon Ko, and Kijung Shin
ICDM 2020 [ paper | video | slides | code and datasets | bib ]
Selected as one of the best papers of ICDM 2020 for fast-track journal invitation

[C26]
Hypergraph Motifs: Concepts, Algorithms, and Discoveries

Geon Lee, Jihoon Ko, and Kijung Shin
VLDB 2020 [ paper | appendix | video | slides | code and datasets | bib ]

[C25]
Incremental Lossless Graph Summarization

{Yunbum Kook, Jihoon Ko}, and Kijung Shin
KDD 2020 [ paper | video (short) | video (long) | slides | code and datasets | bib ]

[C24]
SSumM: Sparse Summarization of Massive Graphs

{Kyuhan Lee, Hyeonsoo Jo}, Jihoon Ko, Sungsu Lim, and Kijung Shin
KDD 2020 [ paper | video (short) | video (long) | slides | code and datasets | bib ]

[C23]
Structural Patterns and Generative Models of Real-world Hypergraphs

Manh Tuan Do, Se-eun Yoon, Bryan Hooi, and Kijung Shin
KDD 2020 [ paper | appendix | video (short) | video (long) | slides | code and datasets | bib ]

[C22]
How Much and When Do We Need Higher-order Information in Hypergraphs?
A Case Study on Hyperedge Prediction

Se-eun Yoon, Hyungseok Song, Kijung Shin, and Yung Yi
WWW 2020 (Short Paper) [ paper | appendix | video | slides | code | bib ]

[C21]
TellTail: Fast Scoring and Detection of Dense Subgraphs

Bryan Hooi, Kijung Shin, Hemank Lamba, and Christos Faloutsos
AAAI 2020 [ paper | appendix | code | bib ]
Oral Presentation

[C20]
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

Siddharth Bhatia, Bryan Hooi, Minji Yoon, Kijung Shin, and Christos Faloutsos
AAAI 2020 [ paper | code and datasets | bib ]
Oral Presentation

[J8]
Temporal Locality-Aware Sampling for Accurate Triangle Counting in Real Graph Streams

Dongjin Lee, Kijung Shin, and Christos Faloutsos
The VLDB Journal [ paper | shorter ver. [C12] | code and datasets | bib ]

[J7]
Fast and Memory-Efficient Algorithms for High-Order Tucker Decomposition

Jiyuan Zhang, Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, and Hwanjo Yu
Knowledge and Information Systems [ paper | shorter ver. [C7] | code | bib ]

[J6]
Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams

Kijung Shin, Sejoon Oh, Jisu Kim, Bryan Hooi, and Christos Faloutsos
ACM TKDD [ paper | shorter ver. [C16] | code and datasets | bib ]


2019

[C19]
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach

Minji Yoon, Bryan Hooi, Kijung Shin, and Christos Faloutsos
KDD 2019 [ paper | video | code | bib ]

[C18]
SWeG: Lossless and Lossy Summarization of Web-Scale Graphs

Kijung Shin, Amol Ghoting, Myunghwan Kim, and Hema Raghavan
WWW 2019 [ paper | slides | poster | patent | bib ]

[C17]
SMF: Drift Aware Matrix Factorization with Seasonal Patterns

Bryan Hooi, Kijung Shin, Shenghua Liu, and Christos Faloutsos
SDM 2019 [ paper | code | bib ]

[T3]
Mining Large Dynamic Graphs and Tensors

Kijung Shin
Ph.D. Thesis, Carnegie Mellon University, 2019
[ paper | slides | code and datasets | bib ]


2018

[C16]
Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions

Kijung Shin, Jisu Kim, Bryan Hooi, and Christos Faloutsos
PKDD 2018 [ paper | appendix | longer ver. [J6] | slides | code and datasets | bib ]

[C15]
ONE-M: Modeling the Co-evolution of Opinions and Network Connections

Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen,
Boleslaw Szymanski, Christos Faloutsos, and Nitesh Chawla
PKDD 2018 [ paper | bib ]

[C14]
Discovering Progression Stages in Trillion-Scale Behavior Logs

Kijung Shin, Mahdi Shafiei, Myunghwan Kim, Aastha Jain, and Hema Raghavan
WWW 2018 (Industry Track) [ paper | slides | bib ]

[C13]
Tri-Fly: Distributed Estimation of Global and Local Triangle Counts in Graph Streams

Kijung Shin, Mohammad Hammoud, Euiwoong Lee, Jinoh Oh, and Christos Faloutsos
PAKDD 2018 [ paper | appendix | longer ver. [J9] | slides | code and datasets | bib ]

[J5]
Fast, Accurate and Flexible Algorithms for Dense Subtensor Mining

Kijung Shin, Bryan Hooi, and Christos Faloutsos
ACM TKDD [ paper | shorter ver. [C5] | code and datasets | bib ]

[J4]
Patterns and Anomalies in k-Cores of Real-World Graphs with Applications

Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
Knowledge and Information Systems [ paper | shorter ver. [C6] | code and datasets | bib ]
Taught in courses: MIT (6.886), Special Issue on the Selected Papers from ICDM 2016

[T2]
Mining Large Dynamic Graphs and Tensors: Thesis Proposal

Kijung Shin
Ph.D. Thesis Proposal, Carnegie Mellon University, 2018
[ paper | slides | code and datasets ]


2017

[C12]
WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams

Kijung Shin
ICDM 2017 [ paper | appendix | longer ver. [J8] | slides | code and datasets | bib ]

[C11]
ZooRank: Ranking Suspicious Entities in Time-Evolving Tensors

Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, and Jürgen Pfeffer
PKDD 2017 [ paper | code and datasets | bib ]

[C10]
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
KDD 2017 [ paper | appendix | video | poster | code and datasets | bib ]

[C9]
Why You Should Charge Your Friends for Borrowing Your Stuff

Kijung Shin, Euiwoong Lee, Dhivya Eswaran, and Ariel D. Procaccia
IJCAI 2017 [ paper | slides (short) | slides (long) | bib ]
Media: New Scientist [link]

[C8]
D-Cube: Dense-Block Detection in Terabyte-Scale Tensors

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
WSDM 2017 [ paper | appendix | longer ver. [J10] | slides | code and datasets | bib ]
SIGIR Student Travel Grant

[C7]
S-HOT: Scalable High-Order Tucker Decomposition

Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, and Hwanjo Yu
WSDM 2017 [ paper | longer ver. [J7] | code | bib ]

[J3]
Graph-Based Fraud Detection in the Face of Camouflage

Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, and Christos Faloutsos
ACM TKDD [ paper | shorter ver. [C4] | code | bib ]
Special Issue on the Best Papers from KDD 2016

[J2]
Fully Scalable Methods for Distributed Tensor Factorization

Kijung Shin, Lee Sael, and U Kang
IEEE TKDE [ paper | appendix | shorter ver. [C2] | code and datasets | bib ]


2016

[C6]
CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms

Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
ICDM 2016 [ paper | appendix | longer ver. [J4] | slides | code and datasets | bib ]
Selected as one of the best papers of ICDM 2016 for fast-track journal invitation

[C5]
M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees

Kijung Shin, Bryan Hooi, and Christos Faloutsos
PKDD 2016 [ paper | appendix | longer ver. [5] | slides | code and datasets | bib ]

[C4]
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage

Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos
KDD 2016 [ paper | longer ver. [J3] | code | bib ]
KDD 2016 Best Paper Award [link], CogX 2017 Award for Best Student Paper in AI [link]
Media: NSF [link], WESA [link], TechXplore [link], Stanford Scholar [link], Crain's [link]

[J1]
Random Walk with Restart on Large Graphs Using Block Elimination

Jinhong Jung, Kijung Shin, Lee Sael, and U Kang
ACM TODS [ paper | shorter ver. [C3] | code and datasets | bib ]


2015

[T1]
Scalable Methods for Random Walk with Restart and Tensor Factorization

Kijung Shin
Senior Thesis, Seoul National University, 2015 [ paper ]
Best Thesis Award [link]

[C3]
BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs

Kijung Shin, Jinhong Jung, Lee Sael, and U Kang
SIGMOD 2015 [ paper | longer ver. [J1] | slides | code and datasets | bib ]
Samsung Humantech Paper Award (1st in Computer Science) [link],
Taught in courses: UMich (EECS 598), SIGMOD Student Travel Award


2014

[C2]
Distributed Methods for High-dimensional and Large-scale Tensor Factorization

Kijung Shin and U Kang
ICDM 2014 [ paper | longer ver. [J2] | slides | code and datasets | bib ]
ICDM Student Travel Award

[C1]
Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression

Dongyeop Kang, Woosang Lim, Kijung Shin, Lee Sael, and U Kang
CIKM 2014 [ paper | appendix | bib ]

Software

[ GitHub ]

NetMiner 4 - Social Network Analysis Software

NetMiner is an application software for exploratory analysis and visualization of large network data based on SNA (Social Network Analysis). This tool allows researchers to explore their network data visually and interactively, helps them to detect underlying patterns and structures of the network.
[ web | wiki | free trial ] • Participation: Jan. 2011 - Dec. 2013



Professional Service

Program Committee Member:

ACM WSDM 2022 / IEEE ICDM 2021 / WWW 2021 / IEEE ICDM 2020 / ACM KDD 2020 / WWW 2020 / IEEE ICDM 2019 / ACM KDD 2019 / WWW 2019 / IDEA 2018