섹션 개요

  •  

    모두 접기 모두 펼치기

    This course page is outdated!

    See https://ucilnica2627.fri.uni-lj.si/course/view.php?id=6229 for the new course page.

    • Course staff

      Jure Leskovec (instructor)
      Charilaos Kanatsoulis (co-instructor)
      Lovro Šubelj (teaching assistant)

    • Weekly schedule
      • Lectures: recorded on Tuesdays & Thursdays (recordings)
      • Consultations: online on Mondays at 5:15pm (Zoom)
      • Office hours: online by agreement (Zoom)

    • Course intro

      This course is based on the course CS224W offered at the Stanford University. The course starts on Sep 22nd, 2025 and lasts for ten weeks not including the breaks. The lectures will be recorded at Stanford on Tuesdays and Thursdays (≈three hours per week), and the videos will be available for download through this web page. Weekly consultations will be on Mondays (≈two hours per week), which will include short review of last two lectures by selected students (tentative) and consultations on the coursework (Q&A). We will precisely follow the CS224W schedule, material, grading policy and other, but not the coursework (see below).

      All course communication should be through Piazza! You should not contact or interact with Stanford in any way.

      Students should check CS224W web site and this web page for updates frequently.

    • Course description

      Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. Tentative course outline is available on CS224W web page.

      Students are expected to have the knowledge of basic computer science principles at a level sufficient to write a non-trivial computer program in Python, and are familiar with basic machine learning and data mining, probability theory and linear algebra.

    • Course material

      There is no official text or book for the course. Course slides, handouts, readings and other material will be posted periodically on CS224W and this web page. 

      The books Graph Representation Learning by William L. Hamilton, Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg, and Network Science by Albert-László Barabási are recommended as optional reading.

    • Coursework & grading

      Coursework consists of three homeworks, five programming assignments, and course project proposal, milestone, report and presentation. Students are encouraged to form groups of up to three for the course project.

      Grading will be based 30% on homeworks (10% on each homework), 25% on programming assignments (5% on each assignment) and 45% on mandatory course project (10% on proposal, 5% on milestone, 25% on report and 5% on presentation). Extra credit can be assigned based on course participation and commitment.

      Each student who submits five assignments or more will be graded at the end of the course. Students should register for the exam in StudIS or VIS.

    • Course assignments

      All assignments will be available through CS224W web site and are usually due on Friday at 9:00am (with some exceptions). Two times during the course you can take advantage of late periods. For an assignment due on Friday, late period expires on Tuesday at 9:00am. 

      All assignments must include a cover sheet with signed honor code and must be submitted through (1) Gradescope using course code 8XPRGY and (2) eUcilnica using the options below. Failing to submit honor code or all developed programming code will result in 10% deduction.

    • Obvestila 포럼