I am looking for students of all levels interested in data mining, graph algorithms, and scalable machine learning.

The application deadlines for KAIST graduate programs are as follows:
- April 16, 2019 for domestic students (click here for more info),
- April 9, 2019 for international students (click here for more info).
If interested, please send me an email.

Kijung Shin (신기정)

Assistant Professor
Data Mining Lab, KAIST EE

About Me

I am an Assistant Professor in the School of Electrical Engineering at KAIST. My research interests include data mining, graph mining, and scalable machine learning. I received my Ph.D. and M.S. 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 my B.A. in Economics from Seoul National University.

Contact Details

Email: kijungs (at) kaist.ac.kr
WWW: https://kijungs.github.io
Address:
School of Electrical Engineering, KAIST
291 Daehak-ro, Yuseong-gu
Daejeon 34141, 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 EE209(B) Programming Structure for Electrical Engineering

Instructor • Spring 2019 [ www ]

CMU 10-601 Introduction to Machine Learning

Teaching Assistant • Fall 2017 [ www ]

CMU 15-780 Graduate Artificial Intelligence

Teaching Assistant • Spring 2017 [ www ]

Publications

[ Google Scholar | DBLP | Research Gate ]


2019 or Later

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

Kijung Shin, Amol Ghoting, Myunghwan Kim, and Hema Raghavan
TheWebConf 2019 (formerly WWW) [ paper | poster | bib ]

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

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

[O5]
Mining Large Dynamic Graphs and Tensors

Kijung Shin
Ph.D. Thesis, Carnegie Mellon University, 2019
[ paper | slides | www (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 | slides | www (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
TheWebConf 2018 (formerly WWW) (Industry Track) [ paper | slides | bib ]

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

Kijung Shin, Bryan Hooi, and Christos Faloutsos
TKDD Journal [ paper | shorter ver. [C5] | www (code and datasets) | 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 | slides | www (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
KAIS Journal [ paper | shorter ver. [C6] | www (code and datasets) | bib ]
Taught in courses: MIT (6.886), Special Issue on the Selected Papers from ICDM 2016

[O4]
Mining Large Dynamic Graphs and Tensors: Thesis Proposal

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


2017

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

Kijung Shin
ICDM 2017 [ paper | appendix | slides | www (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 | poster | www (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]

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

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

[O3]
Patterns and Anomalies in k-Cores of Real-world Networks

Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
NetSci 2017 (Abstract) [ paper | longer ver. [C6] | longest ver. [J4] ]

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

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
WSDM 2017 [ paper | appendix | slides | www (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 | www (code) | bib ]

[J2]
Fully Scalable Methods for Distributed Tensor Factorization

Kijung Shin, Lee Sael, and U Kang
TKDE Journal [ paper | appendix | shorter ver. [C2] | www (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 | www (code and datasets) | bib ]
Selected as one of the best papers of ICDM 16 and invited for potential publication at the KAIS Journal

[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 | www (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
TODS Journal [ paper | shorter ver. [C3] | www (code and datasets) | bib ]

[O2]
Incorporating Side Information in Tensor Completion

{Hemank Lamba*, Vaishnavh Nagarajan*, Kijung Shin*, and Naji Shajarisales*}
WWW Companion 2016 [ paper | bib ]


2015

[O1]
Scalable Methods for Random Walk with Restart and Tensor Factorization

Kijung Shin
Senior Thesis, Dept. of Computer Science and Engineering, 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 | www (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 | www (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.
[ www | wiki | free trial ] • Participation: Jan. 2011 - Dec. 2013



Professional Service

Program Committee Member:

KDD 2019 [www], WWW 2019 [www], IDEA 2018 [www]