TU Delft
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2016/2017 Electrical Engineering, Mathematics and Computer Science Master Electrical Engineering
Modeling and Data Analysis in Complex Networks
Responsible Instructor
Name E-mail
H. Wang    H.Wang@tudelft.nl
Contact Hours / Week x/x/x/x
Education Period
Start Education
Exam Period
Course Language
Course Contents
This course will provide two variants for the last 3 lectures, one focusing on complex network/system performance analysis and design (suggested for EE students) and one focusing on networked data analysis (suggested for Computer Science students)

EE variant: This course introduces the basic tools/metrics to characterize properties of large networks, methods to analyze the dynamic processes such as epidemic/information spread, percolation and opinion dynamics on networks. These tools are applied to understand the effect of network on the function of a system, for example, (a) to evaluate the robustness of infrastructures such as metro transportation networks against failures; (b) to estimate the epidemics/virus spread on social networks/Internet; (c) to explore how properties of brain networks may predict brain functioning like IQ. Such fundamental understanding of the role of a network in its functioning will be further used in the design of a robust possibly interconnected networks against e.g. failures and epidemics.

CS variant: Big Data is mostly obtained from features of components and the interactions among components in large complex systems. Examples are (1) end user features and interactions in both online and real-world social networks like Twitter, (2) data from content sharing platforms such as YouTube (3) physiological data of the brain and (4) stock prices in economic systems. Such dataset is networked in nature i.e. the data of the system components or interactions are (cor)related to each other. This course introduces the basic methodologies to analyze, interpret, model, and possibly to predict such Networked Data, combining advances from network science, modeling of dynamic processes and statistical physics, beyond curve fitting and machine learning. These methods will be applied to diverse real-world datasets such as LinkedIn, Youtube, recommender systems, the brain etc.
Study Goals
EE variant:
After this course, students could represent/abstract a complex system such as a brain or a communication system as a complex network, understand the basic methods to analyze properties of networks and dynamic processes on the networks, design robust networks against e.g. failures and epidemics and be able to apply them to real-world complex systems.

CS variant:
After this course, students could construct the network based on the dataset, characterize and model the network, model the data via e.g. dynamic processes (e.g. viral information spreading) on networks, in order to possibly predicate the future e.g. the popularity of a product, news, or a social network and the prevalence of a disease/computer virus.

Both variants: Students could obtain an overview of the Msc/Phd projects on the frontiers of complex networks and networked data analysis.
Education Method
In total, there will be 7 lectures where one lecture is given by a guest lecturer on the applications in one specific domain e.g. economy, social networks and the brain.
Assessment is based on both homework assignments and the exam (or project).
The homework requires basic programming (in e.g matlab or C)