U Kang Associate Professor Data Mining Lab Department of Computer Science and Engineering Seoul National University 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea 08826 Email: ukang at snu.ac.kr Office: Room 301-502

I am an associate professor in the Department of Computer Science and Engineering at Seoul National University. Here is my short biography and full Curriculum Vitae.
I lead the Data Mining Lab in Department of Computer Science and Engineering.
I also lead a start-up company DeepTrade on AI trading.

(Announcement)

1. I am looking for motivated Ph.D. student, MS students, and postdocs. If interested, send me an email with your full Curriculum Vitae and transcript.
2. I am also looking for undergraduate students who are interested in data mining, machine learning, and artificial intelligence. If interested, send me an email with your full Curriculum Vitae and transcript.
3. Our lab holds the weekly Data Mining Seminar which is open to faculty, graduate, and undergraduate students interested in data mining. Free pizza or sandwitches are provided in the seminar!

What's New

• (Dec. 2019) In the last 3 years, we published 15 top conference papers in the areas of AI, Data Mining, and Big Data: NeurIPS'19, BigData'19, IROS'19, IJCAI'19, CIKM'18, ICDE'18, WWW'18, AAAI'18, WSDM'18, ICDM'17, BigData'17, and SIGMOD'17.
• (Feb 2019) I gave a tutorial on Lightweight Deep Learning with Model Compression in IEEE BigComp 2019.
• (Nov 2018) I won the 10-Year Highest Impact Paper Award from IEEE International Conference on Data Mining (ICDM).
• (Feb 2018) PegasusN, a new version of Pegasus graph mining system is released.
• (Dec 2016) I won the 1st Young Information Scientist Award from Korean Institute of Information Scientists and Engineers.
• (Aug 2013) I won the 2013 SIGKDD Doctoral Dissertation Award, Honorable Mention.
• (Mar 2013) I won the 2013 New Faculty Award from Microsoft Research Asia.

Courses

• Spring 2020, M1522.001400: Introduction to Data Mining
• Spring 2020, M2177.004900: Introduction and Practice of IoT, AI, and Big Data
• Fall 2019, M1522.000900: Data Structure
• Fall 2019, M2177.003000: Advanced Data Mining
• Spring 2019, M1522.001400: Introduction to Data Mining
• Spring 2019, M1522.001600: Reinforcement Learning (Topics in Big Data Analytics)
• Fall 2018, M1522.000900: Data Structure
• Fall 2018, M1522.001600: Advanced Deep Learning (Topics in Big Data Analytics)
• Spring 2018, M1522.001400: Introduction to Data Mining
• Spring 2018, 4190.773: Optimization for Machine Learning (Topics in Artificial Intelligence)
• Fall 2017, M1522.000900: Data Structure
• Fall 2017, M1522.001600: Advanced Deep Learning (Topics in Big Data Analytics)
• Spring 2017, M1522.001400: Introduction to Data Mining
• Spring 2017, M1522.001600: Deep Learning (Topics in Big Data Analytics)
• Fall 2016, M1522.000900: Data Structure
• Fall 2016, 4190.209: Computer Engineering Seminar
• Spring 2016, M1522.001400: Introduction to Data Mining
• Fall 2015, M1522.000900: Data Structure

Research Interests

• Deep Learning and Machine Learning
  • Model compression: to build a lightweight and energy-efficient model
  • Fast and efficient learning: AutoML and transfer learning
  • Anomaly detection, prediction, and one-class classification
• Graphs and Tensors
  • Graph mining: ranking, link prediction, relation inference, knowledge graph analysis, and graph summary
  • Graph machine learning: node classification and representation learning
  • Scalable tensor and graph analysis
• Recommender System
  • Recommendation in multi-modality, where multi-modal data, including ratings, social networks, texts, images, and videos are available
  • Sequence recommendation, including video and news recommendation
  • Active recommendation
• Financial Artificial Intelligence (AI)
  • Time series prediction
  • Asset value prediction
  • Fraud detection/prediction, and credit decision

Awards and Honors

• 10-Year Highest Impact Paper Award, IEEE International Conference on Data Mining (ICDM), 2018
• Young Information Scientist Award, Korean Institute of Information Scientists and Engineers, 2016
• SIGKDD Doctoral Dissertation Award, Honorable Mention, 2013
• New Faculty Award from Microsoft Research Asia, 2013
• Best Application Paper Award, at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2011
• Open Source Software Award, Silver, at Open Source Software World Challenge, 2010
• Best Application Paper Award, Runner-up, at IEEE International Conference on Data Mining (ICDM), 2009

• Hyunsik Jeon, HumanTech Award (honorable mention; 4rd in Computer Science), 2019
• SeungCheol Park, Google Travel Award, 2019
• Hyunsik Jeon, BigData Travel Award, 2019
• Jaemin Yoo, Google PhD Fellowship, 2019
• Quan Chun, Best Master's Thesis Award in SNU CSE, Aug. 2019
• Jaemin Yoo, HumanTech Award (honorable mention; 4rd in Computer Science), 2018
• Sejoon Oh, Best Undergraduate Thesis Award in SNU CSE, Aug. 2018
• Jinhong Jung, BK21 Plus Excellent Research Award, 2018
• Sejoon Oh, HumanTech Award (gold; 1st in Computer Science), 2017
• Jun-gi Jang, HumanTech Award (honorable mention; 4rd in Computer Science), 2017
• Jaemin Yoo, Google Travel Award, 2017
• Jinhong Jung, HumanTech Award (silver; 2nd in Computer Science), 2016
• Jungwoo Lee, HumanTech Award (bronze; 3rd in Computer Science), 2016
• Hamyung Park, Google Travel Award, 2016
• Jinhong Jung, Global Ph.D. Fellowship, 2016
• Jinhong Jung, Naver Ph.D. Fellowship, 2016
• Hamyung Park, HumanTech Award (silver; 2nd in Computer Science), 2015
• Kijung Shin and Jinhong Jung, HumanTech Award (gold; 1st in Computer Science), 2014
• Ho Lee, HumanTech Award (honorable mention; 4th in Computer Science), 2014
• Inah Jeon, Qualcomm Innovation Award (fellowship), 2014
• Hamyung Park, HumanTech Award (bronze; 3rd in Computer Science), 2013

Software

I lead the development of PEGASUS (Peta-Scale Graph Mining System), an award-winning open-source software for mining very large graphs in Hadoop and Spark platforms.

Talks and Tutorials

Press (in Korean)

• 강유 서울대 교수, 데이터마이닝 분야 '최고 영향력 논문상' 수상 (2018-11-22)
• 서울대 공대 강유 교수, 젊은정보과학자상 수상 (2016-12-29)

Publications

(Google Scholar, DBLP)

2020

• D-Tucker: Fast and Memory-Efficient Tucker Decomposition for Dense Tensors.
  Jun-Gi Jang and U Kang.
  36th IEEE International Conference on Data Engineering (ICDE) 2020, Dallas, Texas, USA.
  
  
• Accurate News Recommendation Coalescing Personal and Global Temporal Preferences.
  Bonhun Koo, Hyunsik Jeon, and U Kang.
  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2020, Singapore.
  
  
• BalanSiNG: Fast and Scalable Generation of Realistic Signed Networks.
  Jinhong Jung, Ha-Myung Park, and U Kang.
  23rd International Conference on Extending Database Technology (EDBT) 2020, Copenhagen, Denmark, USA.
  [PDF]
  
  
• FlexGraph: Flexible partitioning and storage for scalable graph mining.
  Chiwan Park, Ha-Myung Park, and U Kang.
  PLOS ONE 15(1): e0227032.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference.
  Jaemin Yoo, U Kang, Mauro Scanagatta, Giorgio Corani, and Marco Zaffalon.
  13rd ACM International Conference on Web Search and Data Mining (WSDM) 2020, Houston, Texas, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  

2019

• Knowledge Extraction with No Observable Data.
  Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang.
  Thirty-third Conference on Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada.
  [PDF] [BIBTEX] [HOMEPAGE (Code)]
  
  
• Data Context Adaptation for Accurate Recommendation with Additional Information.
  Hyunsik Jeon, Bonhun Koo, and U Kang.
  IEEE International Conference on Big Data 2019, Los Angeles, CA, USA.
  [PDF] [HOMEPAGE (Code, Data)]
  
  
• Curved-Voxel Clustering for Accurate Segmentation of 3D LIDAR Point Clouds with Real Time Performance.
  SeungCheol Park, Shuyu Wang and U Kang.
  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019, Macao, China.
  [PDF]
  
  
• Belief Propagation Network for Hard Inductive Semi-supervised Learning.
  Jaemin Yoo, Hyunsik Jeon, and U Kang.
  28th International Joint Conference on Artificial Intelligence (IJCAI) 2019, Macao, China.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• High-Performance Tucker Factorization on Heterogeneous Platforms.
  Sejoon Oh, Namyong Park, Jun-Gi Jang, Lee Sael, and U Kang.
  IEEE Transactions on Parallel and Distributed Systems, Apr. 1, 2019.
  [PDF] [BIBTEX]
  
  
• FURL: Fixed-memory and Uncertainty Reducing Local Triangle Counting for Multigraph Streams.
  Minsoo Jung, Yongsub Lim, Sunmin Lee, and U Kang.
  Data Mining and Knowledge Discovery (DMKD), vol. 33, pp. 1225-1253, Sep. 2019.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks.
  Yongsub Lim, Injae Yu, Dongmin Seo, U Kang, and Lee Sael.
  BMC Bioinformatics, vol. 20, no. 381, 2019.
  [PDF] [BIBTEX]
  
  
• S3CMTF: Fast, accurate, and scalable method for incomplete coupled matrix-tensor factorization.
  Dongjin Choi, Jun-Gi Jang, and U Kang.
  PLOS ONE 14(6): e0217316.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Random Walk Based Ranking in Signed Social Networks: Model and Algorithms.
  Jinhong Jung, Woojung Jin, and U Kang.
  Knowledge and Information Systems (KAIS), Springer, May 2019.
  [PDF] [HOMEPAGE (Code, Data)]
  
  
• Fast and Scalable Method for Distributed Boolean Tensor Factorization.
  Namyong Park, Sejoon Oh, and U Kang.
  VLDB Journal, March 2019.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Supervised and Extended Restart in Random Walks for Ranking and Link Prediction in Networks.
  Woojeong Jin, Jinhong Jung, and U Kang.
  PLOS ONE 14(3): e0213857.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Acute kidney injury predicts all-cause mortality in patients with cancer.
  Eunjeong Kang, Minsu Park, Peonggang Park, Namyong Park, Younglee Jung, U Kang, Hee Kyung Kang, Dong Ki Kim, Kwon Wook Joo, Yon Su Kim, Hyung Jin Yoon and Hajeong Lee.
  Cancer Medicine
  [PDF]
  
  

2018

• Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range.
  Jun-gi Jang, Dongjin Choi, Jinhong Jung, and U Kang.
  ACM International Conference on Information and Knowledge Management (CIKM) 2018, Lingotto, Turin, Italy.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Enumerating Trillion Subgraphs On Distributed Systems.
  Ha-Myung Park, Francesco Silvestri, Rasmus Pagh, Chin-wan Chung, Sung-Hyon Myaeng, and U Kang.
  ACM Transactions on Knowledge Discovery from Data (TKDD)), vol. 12, no. 6, pp. 71:1-71:30, Oct. 2018.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Predicting acute kidney injury in cancer patients using heterogeneous and irregular data.
  Namyong Park, Eunjeong Kang, Minsu Park, Hajeong Lee, Hee-Gyung Kang, Hyung-Jin Yoon, and U Kang.
  PLOS ONE 13(7): e0199839.
  [PDF] [BIBTEX]
  
  
• Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets.
  Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, and U Kang.
  International Journal of Approximate Reasoning (IJAR), vol. 95, pp. 152-166, 2018.
  [PDF] [BIBTEX]
  
  
• Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries.
  Sejoon Oh, Namyong Park, Lee Sael, and U Kang.
  34th IEEE International Conference on Data Engineering (ICDE) 2018, Paris, France.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs.
  Minji Yoon, Jinhong Jung, and U Kang.
  34th IEEE International Conference on Data Engineering (ICDE) 2018, Paris, France.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• SIDE: Representation Learning in Signed Directed Networks.
  Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U Kang.
  The Web Conference (WWW) 2018, Lyon, France.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• 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.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• PegasusN: A Scalable and Versatile Graph Mining System.
  Ha-Myung Park, Chiwan Park, and U Kang.
  Thirty-Second AAAI Conference on Artificial Intelligence (AAAI) 2018, New Orleans, Lousiana, USA.
  (Demo Paper)
  [PDF] [BIBTEX] [HOMEPAGE (Code)]
  
  
• Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs.
  Saehan Jo, Jaemin Yoo, and U Kang.
  11th ACM International Conference on Web Search and Data Mining (WSDM) 2018, Los Angeles, CA, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Memory-efficient and Accurate Sampling for Counting Local Triangles in Graph Streams: From Simple to Multigraphs
  Yongsub Lim, Minsoo Jung, and U Kang.
  ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 12, issue 1, Feburuary 2018.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  

2017

• A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems.
  Haekyu Park, Jinhong Jung, and U Kang.
  IEEE International Conference on Big Data (BigData) 2017, Boston, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks.
  Jaemin Yoo, Saehan Jo, and U Kang.
  IEEE International Conference on Data Mining (ICDM) 2017, New Orleans, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart.
  Jinhong Jung, Namyong Park, Lee Sael, and U Kang.
  ACM International Conference on Management of Data (SIGMOD) 2017, Chicago, IL, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Fast and Scalable Distributed Boolean Tensor Factorization.
  Namyong Park, Sejoon Oh and U Kang.
  IEEE International Conference on Data Engineering (ICDE) 2017, San Diego, CA, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• A New Question Answering Approach with Conceptual Graphs.
  Kyung-Min Kim, Jinhong Jung, Jihee Ryu, Ha-Myung Park, Joseph P.Joohee, Seokwoo Jeong, U Kang, and Sung-Hyon Myaeng.
  Conférence en Recherche d’Information et Applications (CORIA) 2017, Marseille, France.
  [PDF] [BIBTEX]
  
  
• Time-weighted Counting for Recently Frequent Pattern Mining in Data Streams
  Yongsub Lim, and U Kang.
  Knowledge and Information Systems (KAIS), vol. 53, no. 2, pp. 391-422, Sep. 12 2017.
  [PDF] [BIBTEX]
  
  
• Fully Scalable Methods for Distributed Tensor Factorization
  Kijung Shin, Lee Sael, and U Kang.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 29, no. 1, pp. 100-113, Jan. 1 2017.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  

2016

• Partition Aware Connected Component Computation in Distributed Systems.
  Ha-Myung Park, Namyong Park, Sung-Hyon Myaeng, and U Kang.
  IEEE International Conference on Data Mining (ICDM) 2016, Barcelona, Spain.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Personalized Ranking in Signed Networks using Signed Random Walk with Restart.
  Jinhong Jung, Woojung Jin, Lee Sael, and U Kang.
  IEEE International Conference on Data Mining (ICDM) 2016, Barcelona, Spain.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• BIGtensor: Mining Billion-Scale Tensor Made Easy
  Namyong Park, Byungsoo Jeon, Jungwoo Lee, and U Kang.
  ACM International Conference on Information and Knowledge Management (CIKM) 2016, Indianapolis, Indiana, USA.
  (Demo Paper)
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• PIN-TRUST: Fast Trust Propagation Exploiting Positive, Implicit, and Negative Information
  Min-Hee Jang, Christos Faloutsos, Sang-Wook Kim, U Kang, and Jiwoon Ha.
  ACM International Conference on Information and Knowledge Management (CIKM) 2016, Indianapolis, Indiana, USA.
  [PDF] [BIBTEX]
  
  
• PTE: Enumerating Trillion Triangles On Distributed System
  Ha-Myung Park, Sung-Hyon Myaeng, and U Kang.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016, San Francisco, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• M-Flash: Fast Billion-scale Graph Computation Using a Bimodal Block Processing Model
  Hugo Gualdron, Robson Cordeiro, Jose Rodrigeus-Jr, Duen Horng (Polo) Chau, Minsuk Kahng, and U Kang.
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD) 2016, Riva Del Garda, Italy.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• MTP: Discovering High Quality Partitions in Real World Graphs
  Yongsub Lim, Won-Jo Lee, Ho-Jin Choi, and U Kang.
  World Wide Web Journal
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Mining Billion-Scale Tensors: Algorithm and Discoveries
  Inah Jeon, Evangelos E. Papalexakis, Christos Faloutsos, Lee Sael, and U Kang.
  VLDB Journal, vol. 25, issue 4, pp. 519-544, August 2016.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• SCouT: Scalable Coupled Matrix-Tensor Factorization - Algorithm and Discoveries
  ByungSoo Jeon, Inah Jeon, Lee Sael, and U Kang.
  IEEE International Conference on Data Engineering (ICDE) 2016, Helsinki, Finland
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Random Walk with Restart on Large Graphs Using Block Elimination
  Jinhong Jung, Kijung Shin, Lee Sael, and U Kang.
  ACM Transactions on Database Systems (TODS), vol. 41, issue 2, pp. 12:1-12:43, June 2016.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  

2015

• TeGViz: Distributed Tera-Scale Graph Generation and Visualization
  ByungSoo Jeon, Inah Jeon, and U Kang.
  IEEE International Conference on Data Mining (ICDM) 2015, Atlantic City, USA.
  (Demo Paper)
  [PDF] [BIBTEX] [HOMEPAGE (Code)]
  
  
• MASCOT: Memory-efficient and Accurate Sampling for Counting Local Triangles in Graph Streams.
  Yongsub Lim and U Kang.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2015, Sydney, Australia.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs.
  Kijung Shin, Jinhong Jung, Lee Sael, and U Kang.
  ACM International Conference on Management of Data (SIGMOD) 2015, Melbourne, Australia
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• HaTen2: Billion-scale Tensor Decompositions.
  Inah Jeon, Evangelos E. Papalexakis, U Kang, and Christos Faloutsos.
  31st IEEE International Conference on Data Engineering (ICDE) 2015, Seoul, Korea.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Reverse Nearest Neighbor Search with a Non-spatial Aspect.
  JengHoon Park, Chin-Wan Chung, and U Kang.
  Journal of Information Systems.
  [PDF] [BIBTEX]
  
  
• Summarizing and understanding large graphs.
  Danai Koutra, U Kang, Jilles Vreeken, and Christos Faloutsos.
  Statistical Analysis and Data Mining, doi: 10.1002/sam.11267, 18 May 2015.
  [PDF] [BIBTEX]
  
  
• Fast graph mining with HBase.
  Ho Lee, Bin Shao, and U Kang.
  Information Sciences, vol. 315, pp. 56-66, 10 September 2015.
  [PDF] [BIBTEX]
  
  
• Discovering Large Subsets with High Quality Partitions in Real World Graphs.
  Yongsub Lim, Won-Jo Lee, Ho-Jin Choi, and U Kang.
  2nd International Conference on Big Data and Smart Computing (BigComp) 2015, Jeju Island, Korea.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• An Extension of the Automatic Cross-Association Method with a 3-dimensional Matrix.
  Won-Jo Lee, Chae-Gyun Lim, U Kang, and Ho-Jin Choi.
  2nd International Conference on Big Data and Smart Computing (BigComp) 2015, Jeju Island, Korea.
  [PDF] [BIBTEX]
  
  
• Scalable Tensor Mining.
  Lee Sael, Inah Jeon, and U Kang.
  Big Data Research Journal, Feb. 2015.
  [PDF] [BIBTEX]

2014

• Distributed Methods for High-dimensional and Large-scale Tensor Factorization.
  Kijung Shin, and U Kang.
  IEEE International Conference on Data Mining (ICDM) 2014, Shenzhen, China.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Eventera: Real-time Event Recommendation System from Massive Heterogeneous Online Media
  Dongyeop Kang, DongGyun Han, NaHea Park, Sangtae Kim, U Kang, and Soobin Lee.
  IEEE International Conference on Data Mining (ICDM) 2014, Shenzhen, China.
  (Demo Paper)
  [PDF] [BIBTEX]
  
  
• Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams.
  Yongsub Lim, Jihoon Choi, and U Kang.
  23rd ACM International Conference on Information and Knowledge Management (CIKM) 2014, Shaghai, China
  [PDF] [BIBTEX]
  
  
• MapReduce Triangle Enumeration With Guarantees.
  Ha-Myung Park, Franceso Silvestri, U Kang, and Rasmus Pagh.
  23rd ACM International Conference on Information and Knowledge Management (CIKM) 2014, Shaghai, China
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression.
  Dongyeop Kang, Woosang Lim, Kijung Shin, Lee Sael, and U Kang.
  23rd ACM International Conference on Information and Knowledge Management (CIKM) 2014, Shaghai, China
  [PDF] [Supplementary Document] [BIBTEX]
  
  
• MMap: Fast Billion-Scale Graph Computation on a PC via Memory Mapping.
  Zhiyuan Lin, Minsuk Kahng, Kaeser Md. Sabrin, Duen Horng (Polo) Chau, Ho Lee, and U Kang.
  IEEE International Conference on Big Data (BigData) 2014, Washington DC, USA.
  [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
  
  
• SlashBurn: Graph Compression and Mining beyond Caveman Communities.
  Yongsub Lim, U Kang, and Christos Faloutsos.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 26, no. 12, pp. 3077-3089, April 2014.
  [PDF] [BIBTEX] [CODE]
  
  
• Link Prediction Based on Generalized Cluster Information.
  Jungeun Kim, Minsoo Choy, Daehoon Kim, and U Kang.
  23rd International World Wide Web Conference (WWW) 2014, Seoul, Korea.
  (Poster paper)
  [PDF] [BIBTEX]
  
  
• Net-Ray: Visualizing and Mining Billion-Scale Graphs.
  U Kang, Jay-Yoon Lee, Danai Koutra, and Christos Faloutsos.
  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014, Tainan, Taiwan.
  [PDF] [BIBTEX] [HOMEPAGE (Code)]
  
  
• VoG: Summarizing and Understanding Large Graphs.
  Danai Koutra, U Kang, Jilles Vreeken, and Christos Faloutsos.
  SIAM International Conference on Data Mining (SDM) 2014, Philadelphia, Pennsylvania, USA.
  Best of SDM 2014.
  [PDF] [BIBTEX]
  
  
• HEigen: Spectral Analysis for Billion-Scale Graphs.
  U Kang, Brendan Meeder, Evangelos E. Papalexakis, and Christos Faloutsos.
  IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 26, no.2, pp. 350-362, Feb. 2014.
  [PDF] [BIBTEX]
  
  
• Mining Tera-scale graphs with ‘Pegasus’: algorithms and discoveries.
  U Kang and Christos Faloutsos.
  Large Scale Data Analytics, Springer, January 2014. Editors: Aris Gkoulalas- Divanis and Abdel Labbi.
  (Book Chapter)
  [BIBTEX]

2013

• Demonstrating Interactive Multi-resolution Large Graph Exploration.
  Zhiyuan Lin, Nan Cao, Hanghang Tong, Fei Wang, U Kang, and Duen Horng Chau.
  IEEE International Conference on Data Mining (ICDM) 2013, Dallas, Texas, USA.
  (Demo paper)
  [PDF] [BIBTEX]
  
  
• Interactive Multi-resolution Exploration of Million Node Graphs.
  Zhiyuan Lin, Nan Cao, Hanghang Tong, Fei Wang, U Kang, and Duen Horng Chau.
  IEEE VIS 2013, Atlanta, Georgia, USA.
  (Poster paper)
  [PDF] [BIBTEX]
  
  
• Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC.
  Zhiyuan Lin, Duen Horng Chau, and U Kang.
  First International Workshop on Scalable Machine Learning: Theory and Applications, in conjunction with IEEE BigData 2013, Santa Clara, CA, USA.
  [PDF] [BIBTEX]
  
  
• Large Scale Tensor Decompositions: Algorithmic Developments and Applications.
  Evangelos E. Papalexakis, U Kang, Christos Faloutsos, Nicholas D. Sidiropoulos, and Abhay Harpale.
  Bulletin of the Technical Committee on Data Engineering, vol. 36, no. 3, September 2013
  [PDF] [BIBTEX]
  
  
• Big Data Clustering.
  Hanghang Tong and U Kang.
  Data Clustering: Algorithms and Applications, CRC Press, 2013. Editors: Chandan Reddy and Charu Aggarwal.
  (Book Chapter)
  [BIBTEX]
  
  
• Fast anomaly detection despite the duplicates.
  Jay Yoon Lee, U Kang, Danai Koutra, and Christos Faloutsos.
  22nd International World Wide Web Conference (WWW) 2013, Rio de Janeiro, Brazil.
  (Poster paper)
  [PDF] [BIBTEX]
  
  
• Fast and Scalable Approximate Spectral Graph Matching for Correspondence Problems.
  U Kang, Martial Hebert, and Soonyong Park.
  Information Sciences, 2013.
  [PDF] [BIBTEX]

2012

• Big graph mining: algorithms and discoveries.
  U Kang and Christos Faloutsos.
  ACM SIGKDD Explorations Newsletter Volume 14 Issue 2, December 2012. pp. 29-36.
  [PDF] [BIBTEX]
  
  
• GigaTensor: Scaling Tensor Analysis Up By 100 Times - Algorithms and Discoveries.
  U Kang, Evangelos Papalexakis, Abhay Harpale, and Christos Faloutsos.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2012, Beijing, China.
  [PDF] [BIBTEX]
  
  
• GBASE: An Efficient Analysis Platform for Large Graphs.
  U Kang, Hanghang Tong, Jimeng Sun, Ching-Yung Lin, and Christos Faloutsos.
  VLDB Journal, 2012.
  [PDF] [BIBTEX]
  
  
• Mining Tera-Scale Graphs: Theory, Engineering and Discoveries.
  U Kang.
  Ph.D. Thesis, Computer Science Department, Carnegie Mellon Univeristy, May 2012.
  [PDF] [BIBTEX]
  
  
• Large Graph Mining System for Patterns, Anomalies & Visualization.
  Leman Akoglu*, Duen Horng Chau*, U Kang*, Danai Koutra*, and Christos Faloutsos.
  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2012, Kuala Lumpur, Malaysia.
  (Demo paper)
  [BIBTEX]
  
  
• OPAvion: Mining and visualization in large graphs.
  Leman Akoglu*, Duen Horng Chau*, U Kang*, Danai Koutra*, and Christos Faloutsos.
  ACM SIGMOD Conference 2012, Scottsdale, AZ, USA.
  (Demo paper)
  [PDF] [BIBTEX]
  
  
• Axiomatic Analysis of Co-occurrence Similarity Functions.
  U Kang, Mikhail Bilenko, Dengyong Zhou, and Christos Faloutsos.
  CMU Computer Science Tech Report CMU-CS-12-102, February 2012.
  [PDF] [BIBTEX]
  
  
• Fast Random Walk Graph Kernel.
  U Kang, Hanghang Tong, and Jimeng Sun.
  SIAM International Conference on Data Mining (SDM) 2012, Anaheim, California, USA. (acceptance rate 27 %)
  [PDF] [BIBTEX]
  
  
• PEGASUS: Mining Billion-Scale Graphs in the Cloud.
  U Kang, Duen Horng Chau, and Christos Faloutsos.
  IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2012, Kyoto, Japan
  (Special Session: Signal and Information Processing for "Big Data")
  [PDF] [BIBTEX]

2011

• Beyond `Caveman Communities': Hubs and Spokes for Graph Compression and Mining.
  U Kang and Christos Faloutsos.
  IEEE International Conference on Data Mining (ICDM) 2011, Vancouver, Canada. (acceptance rate 12.2 %)
  [PDF] [BIBTEX] [CODE]
  
  
• Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms.
  Danai Koutra, Tai-You Ke, U Kang, Duen Horng (Polo) Chau, Hsing-Kuo Kenneth Pao, and Christos Faloutsos.
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2011, Athens, Greece. (acceptance rate 20.2 %)
  [PDF] [BIBTEX]
  
  
• GBASE: A Scalable and General Graph Management System.
  U Kang, Hanghang Tong, Jimeng Sun, Ching-Yung Lin, and Christos Faloutsos.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2011, San Diego, CA, USA. (acceptance rate 17.5 %)
  [PDF] [BIBTEX]
  
  
• Clustering Very Large Multi-dimensional Datasets with MapReduce.
  Robson L. F. Cordeiro, Caetano Traina Jr., Agma J. M. Traina, Julio Lopez, U Kang, and Christos Faloutsos.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2011, San Diego, CA, USA. (acceptance rate 17.5 %)
  [PDF] [BIBTEX]
  
  
• Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation.
  U Kang, Brendan Meeder, and Christos Faloutsos.
  Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2011, Shenzhen, China. (acceptance rate 9.7 %)
  Best Paper Award: Best Application Paper.
  [PDF] [BIBTEX]
  
  
• Centralities in Large Networks: Algorithms and Observations.
  U Kang, Spiros Papadimitriou, Jimeng Sun, and Hanghang Tong.
  SIAM International Conference on Data Mining (SDM) 2011, Mesa, Arizona, USA. (acceptance rate 25.1 %)
  [PDF] [BIBTEX]
  
  
• Mining Large Graphs: Algorithms, Inference, and Discoveries.
  U Kang, Duen Horng Chau, and Christos Faloutsos.
  IEEE International Conference on Data Engineering (ICDE) 2011, Hannover, Germany. (acceptance rate 19.8 %)
  [PDF] [BIBTEX]
  
  
• HADI: Mining Radii of Large Graphs.
  U Kang, Charalampos E. Tsourakakis, Ana Paula Appel, Christos Faloutsos, and Jure Leskovec.
  ACM Transactions on Knowledge Discovery from Data (TKDD), 2011.
  [PDF] [BIBTEX]
  
  
• PEGASUS: Mining Peta-Scale Graphs.
  U Kang, Charalampos E. Tsourakakis, and Christos Faloutsos.
  Knowledge and Information Systems (KAIS), Springer, 2011.
  [PDF] [BIBTEX]

2010

• Patterns on the Connected Components of Terabyte-Scale Graphs.
  U Kang, Mary McGlohon, Leman Akoglu, and Christos Faloutsos.
  IEEE International Conference on Data Mining (ICDM) 2010, Sydney, Australia. (acceptance rate 19.4 %)
  [PDF] [BIBTEX]
  
  
• Inference of Beliefs on Billion-Scale Graphs.
  U Kang, Duen Horng "Polo" Chau, and Christos Faloutsos.
  Large-scale Data Mining: Theory and Applications (LDMTA) 2010, in conjunction with KDD 2010, Washington D.C., USA.
  [PDF] [BIBTEX]
  
  
• Radius Plots for Mining Tera-byte Scale Graphs: Algorithms, Patterns, and Observations.
  U Kang, Charalampos E. Tsourakakis, Ana Paula Appel, Christos Faloutsos, and Jure Leskovec.
  SIAM International Conference on Data Mining (SDM) 2010, Columbus, Ohio, USA. (acceptance rate 23.4 %)
  [PDF] [BIBTEX]

2009

• PEGASUS: A Peta-Scale Graph Mining System - Implementation and Observations.
  U Kang, Charalampos E. Tsourakakis, and Christos Faloutsos.
  IEEE International Conference on Data Mining (ICDM) 2009, Miami, Florida, USA. (acceptance rate 8.9 %)
  Best Paper Award: Best Application Paper (runner-up).
  [PDF] [BIBTEX] [PEGASUS HOMEPAGE]
  
  
• DOULION: Counting Triangles in Massive Graphs with a Coin.
  Charalampos E. Tsourakakis, U Kang, Gary Miller, and Christos Faloutsos.
  ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2009, Paris, France. (acceptance rate 20 %)
  [PDF] [BIBTEX]

2008

• HADI: Fast Diameter Estimation and Mining in Massive Graphs with Hadoop .
  U Kang, Charalampos Tsourakakis, Ana Paula Appel, Christos Faloutsos, and Jure Leskovec.
  CMU Machine Learning Tech Report CMU-ML-08-117, December 2008.
  [PDF] [BIBTEX]