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
(Dec 2023) Our lab published 10 top conference and journal papers in 2023.
(May 2022) Our paper "DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors" won the Best Research Paper Award (Honorable Mention) at ICDE 2022.
(Aug. 2021) Our paper "Fast and Memory-Efficient Tucker Decomposition for Answering Diverse Time Range Queries" won the Best Research Paper Award at KDD 2021.
(May 2020) Our paper "Accurate News Recommendation Coalescing Personal and Global Temporal Preferences" won the Best Student Paper Award at PAKDD 2020.
Fall 2024, M1522.001400: Introduction to Data Mining
Spring 2024, M1522.000900: Data Structure
Spring 2024, M2177.008600: Introduction to AI
Fall 2023, M3309.000200: Reinforcement Learning
Spring 2023, M1522.000900: Data Structure
Spring 2023, M2177.004900: Introduction and Practice of IoT, AI, and Big Data
Fall 2022, M2177.003000: Advanced Data Mining
Fall 2022, M1522.001400: Introduction to Data Mining
Spring 2021, M3309.000200: Reinforcement Learning
Spring 2021, M1522.001400: Introduction to Data Mining
Fall 2020, M2177.003000: Advanced Data Mining
Fall 2020, M1522.000900: Data Structure
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
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.
Deep Learning and Machine Learning
Model compression (Lightweight model construction, LLM (large language model) compression, and On-device AI)
Reinforcement learning for real-world problems
Graphs and Tensors
Graph neural network
Patterns/models from graphs/tensors, and scalable analysis using GPU and distributed systems
Recommender / Question-Answering (QA) Systems
Diversified and accurate recommendation for news, videos, e-commerce, and finance
Personalized QA system/AI agent with LLM
Trustworthy LLM
Financial Artificial Intelligence (AI)
Time series prediction, time series classification, portfolio management, and optimal trading strategy
Awards and Honors
(My Awards)
Shinyang Engineering Academic Award, 2022
Best Research Paper Award (Honorable Mention), at ICDE, 2022
Best Research Paper Award, at KDD, 2021
Second Prize Award, at MIND News Recommendation Competition, 2020
Best Student Paper Award, at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020
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
Jun-Gi Jang, BK21 Best Graduate Student Award, 2022
Jun-Gi Jang, Naver Ph.D. Fellowship, 2021
Jun-Gi Jang, Qualcomm Innovation Fellowship, 2021
Jun-Gi Jang, Yulchon AI Star Fellowship, 2021
Jun-Gi Jang, KDD Best Research Paper Award, 2021
Jongjin Kim, SNU BK21 Top Talent Fellowship, 2021
Jaemin Yoo, SDM Travel Award, 2021
Jaemin Yoo, BK21 Plus Star Researcher Award, 2021
Jaehun Jung, Best Undergraduate Thesis Award in SNU CSE, 2021
Jaemin Yoo, Qualcomm Innovation Fellowship, 2020
Hyunsik Jeon, SeungCheol Park, Yuna Bae, Second Prize Award, at MIND News Recommendation Competition, 2020
Jaemin Yoo, Yulchon AI Star Fellowship, 2020
Bonhun Koo and Hyunsik Jeon, Best Student Paper Award, at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020
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
Chun Quan, 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 ofPEGASUS (Peta-Scale Graph Mining System), an award-winning open-source software for mining very large graphs in Hadoop and Spark platforms.
Talks and Tutorials
Compressing Large Langauge Models, DASFAA 2024, July 2024
Compressing Large Langauge Models, BigComp 2024, Feb 2024
Predicting Stock Market with AI, SWCS 2023, Apr. 2023
Financial AI, Inha University, Dec. 2022
Graph Neural Networks: Theory and Applications, DB Society, Aug. 2022
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.
Accurate Sublayer Pruning for Large Language Models by Exploiting Latency and Tunability Information
Seungcheol Park, Sojin Lee, Jongjin Kim, Jinsik Lee, Hyunjik Jo, and U Kang.
International Joint Conference on Artificial Intelligence (IJCAI) 2025, Montreal, Canada.
Zero-shot Quantization: A Comprehensive Survey
Minjun Kim, Jaehyeon Choi, Jongkeun Lee, Wonjin Cho, and U Kang.
International Joint Conference on Artificial Intelligence (IJCAI) 2025, Montreal, Canada.
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
Minjun Kim, Jaehyeon Choi, SeungJoo Lee, Jinhong Jung, and U Kang.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2025, Sydney, Australia.
DART: Diversified and Accurate Long-Tail Recommendation
Jeongin yun, Jaeri Lee, and U Kang.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2025, Sydney, Australia.
SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning
Minjun Kim, Jongjin Kim, and U Kang.
International Conference on Learning Representations (ICLR) 2025, Singapore. [PDF]
Sequentially Diversified and Accurate Recommendations in Chronological Order for a Series of Users.
Jongjin Kim and U Kang.
ACM International Conference on Web Search and Data Mining (WSDM) 2025. [PDF]
Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning.
Junghun Kim, Ka Hyun Park, Hoyoung Yoon, and U Kang. AAAI Conference on Artificial Intelligence 2025. [PDF]
Threshold-Based Exploitation of Noisy Label in Black-box Unsupervised Domain Adaptation.
Huiwen Xu, Jaeri Lee, and U Kang.
PLOS ONE
Accurate Coupled Tensor Factorization with Knowledge Graph.
Seungjoo Lee, Yong-chan Park, and U Kang.
IEEE International Conference on Big Data (BigData) 2024, Washington DC, USA. [PDF]
Accurate Stock Movement Prediction via Multi-Scale and Multi-Domain Modeling.
JinGee Kim, Yong-chan Park, Jaemin Hong, and U Kang.
IEEE International Conference on Big Data (BigData) 2024, Washington DC, USA. [PDF]
Towards True Multi-interest Recommendation: Enhanced Scheme for Balanced Interest Training.
Jaeri Lee, Jeongin Yun, and U Kang.
IEEE International Conference on Big Data (BigData) 2024, Washington DC, USA. [PDF]
Domain-Aware Data Selection for Speech Classification via Meta-Reweighting.
Junghun Kim, Ka Hyun Park, Hoyoung Yoon, and U Kang. InterSpeech 2024. [PDF]
Fast and Accurate PARAFAC2 Decomposition for Time Range Queries on Irregular Tensors
Jun-Gi Jang, Yong-chan Park, and U Kang.
ACM International Conference on Information and Knowledge Management (CIKM) 2024. [PDF][BIBTEX][HOMEPAGE (Code, Data)]
FreQuant: A Reinforcement-Learning based Adaptive Portfolio Optimization with Multi-frequency Decomposition
Jihyeong Jeon, Jiwon Park, Chanhee Park, and U Kang.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024, Barcelona, Spain. [PDF][BIBTEX]
Fast Multidimensional Partial Fourier Transform with Automatic Hyperparameter Selection
Yong-chan Park, Jongjin Kim, and U Kang.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024, Barcelona, Spain. [PDF][BIBTEX][HOMEPAGE (Code, Data)]
Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition
Junghun Kim, Ka Hyun Park, and U Kang.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024, Barcelona, Spain. [PDF][BIBTEX][HOMEPAGE (Code, Data)]
Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
Hyunsik Jeon, Jong-eun Lee, Jeongin Yun, and U Kang. The Web Conference 2024, Singapore. [PDF][BIBTEX][HOMEPAGE (Code)]
Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models
Seungcheol Park, Hojun Choi, and U Kang.
International Conference on Learning Representations (ICLR) 2024, Vienna, Austria. [PDF][BIBTEX][HOMEPAGE (Code)]
Accurate Semi-supervised Automatic Speech Recognition via Multi-hypotheses-based Curriculum Learning
Junghun Kim, Kahyun Park, and U Kang.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2024, Taipei, Taiwan. [PDF][BIBTEX][HOMEPAGE (Code, Data)]
A Comprehensive Survey of Compression Algorithms for Language Models
Seungcheol Park, Jaehyeon Choi, Sojin Lee, and U Kang. arXiv:2401.15347 [PDF][BIBTEX]
DDC3N: Doppler-Driven Convolutional 3D Network for Human Action Recognition
Toshpulatov Mukhiddin, Wookey Lee, Suan Lee, Hoyoung Yoon, and U Kang. IEEE Access 12: 93546-93567 (2024) [PDF][BIBTEX]
Representative and Back-In-Time Sampling from Real-world Hypergraphs
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin.
ACM Transactions on Knowledge Discovery and Data (TKDD), 2024. [PDF][BIBTEX]
2023
Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors - Algorithm and Application
Jun-Gi Jang, Jeongyoung Lee, Yong-chan Park, and U Kang.
The 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2023. [PDF][BIBTEX][HOMEPAGE (Code)]
Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance
Huiwen Xu and U Kang.
International Conference on Computer Vision (ICCV) 2023, Paris, France. [PDF][BIBTEX]
Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking.
Hyunsik Jeon, Jongjin Kim, Jaeri Lee, Jong-eun Lee, and U Kang.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2023, Osaka, Japan. [PDF][BIBTEX]
Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation.
Jongjin Kim, Hyunsik Jeon, Jaeri Lee, and U Kang.
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2023, Osaka, Japan. [PDF][BIBTEX]
PET: Parameter-efficient Knowledge Distillation on Transformer.
Hyojin Jeon, Seungcheol Park, Jin-Gee Kim, and U Kang. PLOS ONE [PDF][BIBTEX][HOMEPAGE (Code)]
Accurate Open-set Recognition for MemoryWorkload.
Jun-Gi Jang, Sooyeon Shim, Vladimir Egay, Jeeyong Lee, Jongmin Park, Suhyun Chae, and U Kang.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. [PDF][BIBTEX]
Fast and accurate interpretation of workload classification model.
Sooyeon Shim, Doyeon Kim, Jun-Gi Jang, Suhyun Chae, Jeeyong Lee, and U Kang. PLOS ONE [PDF][BIBTEX]
Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters.
Hyunsik Jeon, Jun-Gi Jang, Taehun Kim, and U Kang. PLOS ONE [PDF][BIBTEX][HOMEPAGE (Code, Data)]
Static and Streaming Tucker Decomposition for Dense Tensors.
Jun-Gi Jang and U Kang.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. [PDF][BIBTEX][HOMEPAGE (Code, Data)]
2022
Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets.
Yejun Soun*, Jaemin Yoo*, Minyong Cho, Jihyeong Jeon, and U Kang.
IEEE International Conference on Big Data (BigData) 2022, Osaka, Japan. [PDF][BIBTEX]
Accurate PARAFAC2 Decomposition for Temporal Irregular Tensors with Missing Values.
Jun-Gi Jang, Jeongyoung Lee, Jiwon Park, and U Kang.
IEEE International Conference on Big Data (BigData) 2022, Osaka, Japan. [PDF][BIBTEX][HOMEPAGE (Code)]
Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
Hyunsik Jeon, Jongjin Kim, Hoyoung Yoon, Jaeri Lee, and U Kang.
ACM International Conference on Information and Knowledge Management (CIKM) 2022. [PDF][BIBTEX]
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, and U Kang.
The 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2022. [PDF][BIBTEX][HOMEPAGE (Code)]
DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense Tensors.
Jun-Gi Jang and U Kang.
38th IEEE International Conference on Data Engineering (ICDE) 2022, Kuala Lumpur, Malaysia. Best Paper Award: Best Research Paper (Honorable Mention). [PDF][BIBTEX][HOMEPAGE (Code, Data)]
MiDaS: Representative Sampling from Real-world Hypergraphs.
Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, and Kijung Shin.
The WebConf 2022, Lyon, France. [PDF][BIBTEX][HOMEPAGE (Code, Data)]
Graph-based PU Learning for Binary and Multiclass Classification without Class Prior.
Jaemin Yoo, Junghun Kim, Hoyoung Yoon, Geonsoo Kim, Changwon Jang, and U Kang. Knowledge and Information Systems (KAIS), Springer, 2022.
[PDF][BIBTEX]
Finding Key Structures in MMORPG Graph with Hierarchical Graph Summarization.
Jun-Gi Jang, Chaeheum Park, Changwon Jang, Geonsoo Kim, and U Kang.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2022. [PDF][BIBTEX]
SensiMix: Sensitivity-Aware 8-bit Index & 1-bit Value Mixed
Precision Quantization for BERT Compression.
Tairen Piao, Ikhyun Cho, and U Kang. PLOS ONE [PDF][BIBTEX][HOMEPAGE (Code)]
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]
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)]
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 (BigData) 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)]
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)]
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)]
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]
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]
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.