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Instructor Location and Time Office Hours |
Amarda Shehu and Fei Li,
ENGR #4452 and #5326, [amarda, lifei]\AT\gmu.edu Art and Design Building #2026, W, 7:30pm - 10:05 pm ENGR#4452, W, 6:20 - 7:19 pm (Shehu) and ENGR#5326, M, 6:20 - 7:19 pm (Li) |
The objective of this course is to introduce students to complex systems and network-based treatments of such systems. Complex systems, whether living or abstract, can be represented as static or dynamic networks of many interacting components. They are typically composed of components that are much simpler in behavior or function than the system. The complex behavior of a multi-component system is an emergent property of the network that can be constructed to describe the local relationships between the components that make up the system.
Network science is a new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems. Applications of network science address physical, informational, biological, cognitive, and social systems.
In this course, we will emphasize the fundamental underpinnings of network science to graph-theoretic concepts and graph algorithms. We will study algorithmic, computational, and statistical methods of network science. We will also highlight diverse applications in machine learning, robotics, communications, biology, ecology, brain science, sociology, and economics. The course will go beyond the strictly structural concepts of small-world and scale-free networks, focusing on dynamic network processes such as epidemics, synchronization, or adaptive network formation.
Target audience: Graduate students that have completed two core courses. Students are encouraged to contact the instructors for more information on what fundamental background knowledge is assumed for students to do well in this course.
Format: Active participation by students is expected via various venues. Students will also provide "mini-talks" (10 minutes) on specific topics that will be announced in class. Students should also actively participate in discussions during the lectures. Attendance is mandatory. Student have some discretion regarding programming language for homeworks and final project contingent upon instructor approval.
Categorization: The final project, whose topic needs to be negotiated with the instructors, determines the area satisfied by this course. Both instructors need to agree on the area based on the topic of the project.
Prerequisites:Two core courses and permission of instructor.
Textbook(s):The course will combine topics from various textbooks and research literature. Material will be disseminated mainly in the form of lectures. Supplementary online reading materials will also be provided.
| Date | Topic | Lectures | Assignments |
|---|---|---|---|
| Aug. 31 | Overview of Network Science, History, and Relation to Graph Theory |
| Network Metrics |
| Sep. 07 | Network Analysis Metrics and Relation to Graph-theoretic Concepts | Hw1 Out | |
| Sep. 14 | Emergent Properties of Real Networks |
| Network Models |
| Sep. 21 | Random Network Models and Preferential Attachment | Hw1 Due | |
| Sep. 28 | Optimization-based Network Formation Models |
| Algorithms for Network Community Detection |
| Oct. 05 | Graph Partitioning, Modularity Maximization, and Hierarchical Divisive and Agglomerative Methods | Hw2 Out | |
| Oct. 12 | Overlapping, Dynamic Communities and their Properties |
| Network Mining and Machine Learning Methods | ||
| Oct. 19 | Student Mini-talks, Anomaly Detection | Hw2 Due | |
| Oct. 26 | Graph Summarization, Guest Lecture | Hw3 Out |
| Network Dynamics | ||
| Nov. 02 | Percolation, Resilience, Growth, and Rewiring | ||
| Nov. 09 | Epidemics, Guest Lecture | ||
| Nov. 16 | Social Networks, Guest Lecture | Hw3 Due |
| Additional Topics (Student Interest-Driven) | ||
| Nov. 30 | Statistical Analysis
of Network Data, Power Grid Analysis Co-evolutionary Dynamics, Adaptive Networks |
Project Topic Selection | |
| Dec. 07 | Temporal Networks, Games on Networks | ||
| Dec. 14 | Project Demos and Presentations | Report due before class |
The class enforces the GMU Honor Code. Violations of academic honesty will not be tolerated.
If a disability or other condition affects your academic performance, document it with the Office of Disability Services.
Latest lectures and other course materials will be available at
URL
http://www.cs.gmu.edu/~ashehu/?q=CS695_Fall2016