U Kang
U Kang
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 a Professor in the Department of Computer Science and Engineering at Seoul National University. Here is my short biography and full Curriculum Vitae.
I am also a Head Professor in the Interdisciplinary Program in Artificial Intelligence at Seoul National University.
I lead the Data Mining Lab in Department of Computer Science and Engineering.
I founded and advise DeepTrade, a company providing a state-of-the-art AI trading technology.

(Announcement)

  1. I am looking for motivated Ph.D. students and MS students. If interested, send me an email with your full Curriculum Vitae and transcript.

What's New

Courses

Current Semester

Previous Semesters

Research Interests

My research interests are large-scale Data Mining and Machine Learning. I develop core methods in AI and data science for 1) predictive modeling and anaylyzing real world data (e.g., graphs, tensors, time series, text, etc.), and 2) fast/adaptive inference and learning. Applications include recommender system, financial AI, industrial machine learning, social network, and anomaly detection.

Awards and Honors

     (My Awards)      (My Students' Awards)

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

  • Recommender system, Posco ICT, Nov. 2021.
  • Financial AI, Naver TV, Oct. 2021.
  • Data mining, Naver TV, Sep. 2021.
  • Stock prediction with AI, NC Soft, July 2021.
  • Time series analysis, LG AI Research, March 2021.
  • Lightweight deep learning, Kakao Enterprise, Feb. 2021.
  • Designing lightweight deep learning model, SNU AI Summer School, August 2020.
  • Recent Advancement in Recommendation and Model Compression, GIST AI Summer School, July 2020.
  • AI Technologies for Finance, KCC, July 2020.
  • Financial AI, KTB Security, Feb. 2020.
  • Knowledge Extraction with No Observable Data, KCC, Dec. 2019.
  • Deep Recommender System, KIBME Workshop, July 2019.
  • Lightweight Deep Learning with Model Compression, IEEE BigComp 2019, Kyoto, Japan, Feb. 2019.
  • Large Scale Tensor Analysis, 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2017, Jeju, Korea, May 2017.
  • BigTensor: Mining Billion-Scale Tensor Made Easy, Intel Labs, Santa Clara, CA, USA, Aug. 2016.
  • Fast Random Walk with Restart for Ranking in Large Graphs, Oracle Labs, Redwood City, CA, USA, Aug. 2016.
  • BEAR: Block Elimination Approach for Random Walk With Restart on Large Graphs, Korea-Japan Database Workshop (KJDB) 2015, Okinawa, Japan, Dec. 4, 2015.

    Press (in Korean)

    Publications

    (Google Scholar, DBLP)

    2024

    2023

    2022

    2021

    • Accurate Graph-Based PU Learning without Class Prior.
      Jaemin Yoo, Junghun Kim, Hoyoung Yoon, Geonsoo Kim, Changwon Jang, and U Kang.
      IEEE International Conference on Data Mining (ICDM) 2021, Auckland, New Zealand.
      [PDF] [BIBTEX]
      Best Ranked Papers for KAIS Journal Invitation.

    • Accurate Online Tensor Factorization for Temporal Tensor Streams with Missing Values
      Dawon Ahn, Seyun Kim, and U Kang.
      ACM International Conference on Information and Knowledge Management (CIKM) 2021.
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]

    • Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts
      Jaemin Yoo, Yejun Soun, Yong-chan Park, and U Kang.
      The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2021.
      [PDF] [BIBTEX]

    • Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries
      Jun-Gi Jang and U Kang.
      The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2021.
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]
      Best Paper Award: Best Research Paper.

    • Fast and Accurate Partial Fourier Transform for Time Series Data
      Yong-chan Park, Jun-Gi Jang, and U Kang.
      The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2021.
      [PDF] [BIBTEX] [HOMEPAGE (Code)]

    • Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion
      Jaehun Jung, Jinhong Jung, and U Kang.
      The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2021.
      [PDF] [BIBTEX] [HOMEPAGE (Code)]

    • Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting
      Jaemin Yoo and U Kang.
      SIAM International Conference on Data Mining (SDM) 2021.
      [PDF] [BIBTEX]

    • PGT: News Recommendation Coalescing Personal and Global Temporal Preferences.
      Bonhun Koo, Hyunsik Jeon, and U Kang.
      Knowledge and Information Systems (KAIS), Springer, 2021.
      [PDF] [BIBTEX] [HOMEPAGE (Code)]

    • Transfer alignment network for blind unsupervised domain adaptation.
      Huiwen Xu and U Kang.
      Knowledge and Information Systems (KAIS), Springer, Sep. 2021.
      [PDF] [BIBTEX] [HOMEPAGE (Code)]

    • Compressing deep graph convolution network with multi-staged knowledge distillation.
      Junghun Kim, Jinhong Jung, and U Kang.
      PLOS ONE
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]

    • Multi-EPL: Accurate multi-source domain adaptation.
      Seongmin Lee, Hyunsik Jeon, and U Kang.
      PLOS ONE
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]

    • AUBER: Automated BERT Regularization.
      Hyun Dong Lee*, Seongmin Lee*, and U Kang.
      PLOS ONE
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]

    • Unsupervised Multi-Source Domain Adaptation with No Observable Source Data.
      Hyunsik Jeon, Seongmin Lee, and U Kang.
      PLOS ONE
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]

    2020

    • FALCON: Lightweight and Accurate Convolution.
      Jun-Gi Jang, Chun Quan, Hyun Dong Lee, and U Kang.
      arXiv:1909.11321v2.
      [PDF] [BIBTEX]

    • Gtensor: Fast and Accurate Tensor Analysis System using GPUs
      Dawon Ahn, Sangjun Son, and U Kang.
      ACM International Conference on Information and Knowledge Management (CIKM) 2020.
      (Demo Paper)
      [PDF] [BIBTEX] [HOMEPAGE (Code)]

    • 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.
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]

    • 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.
      Best Paper Award: Best Student Paper.
      [PDF] [BIBTEX]

    • 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] [BIBTEX] [HOMEPAGE (Code, Data)]

    • Accurate Relational Reasoning in Edge-labeled Graphs by Multi-Labeled Random Walk with Restart
      Jinhong Jung, Woojeong Jin, Ha-myung Park, and U Kang.
      World Wide Web Journal
      [PDF] [BIBTEX]

    • PACC: Large scale connected component computation on Hadoop and Spark.
      Ha-Myung Park, Namyong Park, Sung-Hyon Myaeng, and U Kang.
      PLOS ONE 15(3): e0229936.
      [PDF] [BIBTEX] [HOMEPAGE (Code, Data)]

    • 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] [BIBTEX]

    • 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


    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]

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