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 courses offered by LIACS on these topics.
The course can be taken in parallel to Complex Networks (BM) or after having done the Leiden minor Network Science for a Connected World.
Description
This course deals with the computer science aspects of social network analysis (SNA); or more broadly, it serves as an introduction to 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 (scientific) collaboration networks; but perhaps most notably; (online) social networks; such as Facebook; Instagram and Twitter. With millions of nodes (users) and possibly 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; influence spread and epidemic spread modelling.
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.
Understand how to solve real-world problems in networked systems using quantitative measures and SNA algorithms.
Evaluate the outcomes and findings of SNA algorithms on real-world social network data.
Schedule
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.
Teaching method
Lectures
Lab sessions
Individual assignments
All activities take place on-site; while materials (e.g., slides) are digitally available; there is no option for online participation.
Assesment method
Grade composition
40% - Two assignments
60% - Exam
Other assessment aspects
All components of the grade should be sufficient (>= 5.5) in order to pass the course.
Students are expected to know the rules regarding plagiarism and originality of handed in work. A small sample of students may be subjected to a short individual oral evaluation to confirm the originality of handed in work.
Partial grades that are sufficient can be carried on to the next year.
A retake opportunity exists for each component of the grade.
Resit, review & feedback
Assignments are returned to students for review together with the publication of the grades on Brightspace. The next lab session can be utilized to obtain feedback from course staff or ask questions. The lecturer will communicate a moment for the exam review at the beginning of the semester.
Reading list
Lecture slides; online materials 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: prof. 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.
Note: For this course; you do not need to make use of this software distribution platform. We use open-source software and (Python) packages.