General
Welcome to the Wonderful World of Networks!
Nature is full of networks, from the intracellular machinery inside us to the complex networks of neurons that form our brains, and to the vast number of networks formed by humans, from off- and online social networks to technological systems such as the Internet.
These networks are important from many points of view - understanding how they work is essential for understanding many natural phenomena, from spreading of diseases to information flows in social networks, and from cascading power grid failures to financial crises.
The Complex Networks course provides you an introduction to the modern theory of networks, together with skills for applying this knowledge to real-world networks and empirical data.
The course consists of lectures and course assignments (= weekly exercises and a project work). There is no exam - grading is based on points from the course assignments (60% needed for passing the course). Assignments comprise some pen-and-paper maths but mainly computer exercises with Python. Thus, elementary programming skills are required. We'll provide a short Python tutorial and introduce the networkX package used in this course. Solving the exercises requires some time each week, so please plan your studies accordingly.
The course will be lectured by Jari Saramäki (jari.saramaki@aalto.fi). Course assistants are Onerva Korhonen (onerva.korhonen@aalto.fi), Tuomas Alakörkkö, Arash Badie Modiri, Sara Heydari, Takayuki Hiraoka, Tarmo Nurmi, Ana Triana, Silja Sormunen, and Javier Ureña Carrion. The preferred email address for contacting the course staff is cs-e5740@aalto.fi.
In autumn 2020, the course will be organized fully online with video lectures and exercise sessions via Zoom. We'll update this page with further details by beginning of September.
Welcome to the course, and please do not forget to register via Oodi! For students coming from other universities than Aalto, please obtain an Aalto user account before attending the course.
All course materials will be published in MyCourses before the first lecture (9.9.). Before that, you can have a look at last year's materials to get an idea.