Data Mining Lab.

Welcome to Data Mining Laboratory in the Department of Computer Science and Engineering at Seoul National University. Our research interests lie in big data mining which aims to find models, algorithms, and systems for scalable data analysis with applications on knowledge discovery, learning, and anomaly detection. Specifically, we focus on the following research topics: deep learning & scalable machine learning, recommendation system, big graph mining, big tensor mining, online stream mining, and anomaly/fraud detection.

What's New

  • [Jan 2018] 2 students in DM Lab. won HumanTech Award.

    B.S. student Sejoon Oh won the HumanTech award (gold, 1st in Computer Science) from Samsung, from his paper "Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries". M.S/Ph.D. student Jun-gi Jang also won the Humantech award (honorable mention, 4th in Computer Science), from his paper "Fast and Memory-Efficient Method for Time Ranged Singular Value Decomposition". HumanTech award is given to best papers in the engineering area, and it is the most prestigious award among such kind in Korea. Congratulations!

  • [Dec 2017] 2 papers accepted to ICDE 2018, a top tier database conference.

    2 papers are accepted to ICDE 2018, a top tier database conference. The paper "TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs" proposed an algorithm which computes RWR scores approximately exploiting PageRank and block-wise structure of real-world graphs. The paper "Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries" proposed a scalable Tucker factorization method for sparse tensors, which updates factor matrices in a row-wise manner.

  • [Dec 2017] 2 papers accepted to The Web Conference 2018, a top tier data mining conference.

    2 research track papers are accepted to The Web Conference 2018, a top tier data mining conference. The paper "SIDE: Representation Learning in Signed Directed Networks" proposed a network embedding algorithm for signed directed networks. The paper "Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees" proposed an algorithm computing Random Walk with Restart efficiently on dynamic graphs.

  • [Nov 2017] A paper accepted to AAAI 2018, a top tier artificial intelligence conference.

    The demo paper "PegasusN: A Scalable and Versatile Graph Mining System" is accepted to AAAI 2018, a top tier artificial intelligence conference. The paper proposed PegasusN that provides and seamlessly integrates scalable algorithms for various graph mining operations: graph structure analysis, subgraph enumeration, graph generation, and graph visualization.

  • [Oct 2017] Jaemin Yoo won Google Travel Award

    Ph.D. student Jaemin Yoo won Google Travel Award for his ICDM 2017 conference trip. Google Travel Award is given to a selected primary author of a paper in a top-tier conference in Computer Science. Jaemin will present his paper "Supervised Belief Propagation: Scalable Supervised Inference on Attributed Networks" in ICDM 2017. Congratulations!

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Data Mining Seminar

  • Every Tuesday, 18:00 - 19:00
  • #551, Building 301, Seoul National University

  • May 15 Jinhong Jung

    SlashBurn: Graph Compression and Mining beyond Caveman Communities

  • May 8 Ha-Myung Park

    Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking

  • May 1 Taebum Kim

    Training Compressed Fully-Connected Networks With a Density-Diversity Penalty

  • April 10 Minji Yoon

    TPA: Fast, Scalable and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs

  • April 3 Sejoon Oh

    Scalable Tucker Factorization for Sparse Tensors