Admission requirements
Recommended prior knowledge: It is highly recommended that students have knowledge of algorithms, data structures and preferably also machine learning or data mining. See for example the content of the respective Leiden Bachelor Informatica courses.
Description
This course deals with the computer science aspects of social network analysis, or more broadly, the field of network science. This field, with strong roots in computer science, aims to understand the real-world by analyzing interactions between the individuals in it. Examples of such networks include webgraphs, communication (telephone, email) and collaboration networks, but perhaps most notably (online) social networks, such as Facebook, Instagram and Twitter. With millions of nodes (users) and possible billions of links (interacttion between users), traditional graph algorithms are often too complex and unable to solve trivial algorithmic and data mining related problems.
Typical topics and research problems in this field include fundamental problems such as efficient retrieval, storage, anonymization and compression of graph data and computational problems such as computing shortest paths, diameter and other descriptive graph properties. Other topics include community detection, outlier detection, link prediction, information diffusion and epidemic spread.
Course objectives
At the end of this course, students should be able to:
Define how graph theoretical concepts, graph algorithms and data structures are used in social network analysis (SNA) methods.
Apply and make hands-on use of SNA methods and algorithms through programming and using software packages.
Analyze the theoretical complexity and empirical performance of SNA algorithms on benchmark data.
Evaluate the outcomes and findings of SNA algorithms on real-world social network data.
Explain a scientific paper in SNA and communicate this in the form of a presentation.
Critically analyze and position a state-of-the-art approach in SNA proposed in the literature.
Propose a modest scientific contribution in the field of SNA, e.g., a replication study or new algorithm.
Collaboratively write a scientific paper in which one or more existing or newly designed approaches are analyzed and compared.
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Lectures
Seminars
Lab sessions
Individual assignments
Group course project
All activities take place on-site; while material is digitally available, there is no option for online participation.
Assessment method
Grade composition
40% - Two assignments (individual)
60% - Course project (in groups), consisting of:
20% (1/3 of course project) - Presentation (graded individually)
40% (2/3 of course project) - Paper project
Other assessment aspects
All components of the grade should be sufficient (>= 5.5) in order to pass the course.
Participation in an in-class peer review process halfway through the course is mandatory.
Students are expected to know the rules regarding plagiarism and originality of handed in work. A small random sample of students may be subjected to a short individual oral evaluation to confirm the originality of handed in work.
Because of the group work in the second half of the course, students who choose to drop the course (e.g., becasuse it is an elective) and do not want to receive a final grade, are expected to deregister for the course no later than in week 5.
Partial grades that are sufficient can be carried on to the next year, with the exception of the presentation of the course project.
A retake opportunity exists for each element, with the exception of the presentation of the course project.
Reading list
Lecture slides and provided papers (no book).
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturer: dr. Frank Takes
Course website: SNACS
Brigthspace is used for announcements, handing in student work and communicating grades.
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.