This course explores the frontiers of Network Science, combining foundational concepts with recent advances and emerging research in the field. Classes will integrate selected textbook chapters with current research articles, with an emphasis on connecting mathematical foundations to contemporary research questions. Topics include graphs and networks; random networks and different classes of scale-free networks; network properties such as assortativity, motifs, resilience, and robustness; social networks and community structure; and the dynamics of processes on networks. The course will also examine recent developments and open problems that are shaping the future of network science.
This course aims to provide a strong foundation in network science and resilience in preparation for jobs in industry and research. After taking this course, you, the student, should be able to:
Understand the structures and dynamics of networked systems;
Apply the concept of network resilience to real-world systems in different fields;
Build computer programming skills for network analysis and network visualization;
Understand the principles of applying network science to disciplinary science and design and set up basic models for some specific applications.
Additionally, graduate students will also be able to:
Read, analyze, and critique published literature in the field of network science and dynamical systems.
Assess novelty of network science research and its relation to the state of the art in the field.
Network Science, Albert-László Barabási, Combridge University Press, 2016
On-line version is available at http://barabasi.com/networksciencebook/
CSCI-2300; a 4000 level algorithms-based CSCI (e.g., 4020, 4050, 4260, 4800), or MATH (4100, 4150, 4200, 4210, 4800) course; junior or senior level standing; some familiarity with probability theory, linear algebra, and calculus; or permission of the instructor.
Theory, Algorithms, and Mathematics
Artificial Intelligence and Data
Introduction
Graph theory
Random networks
Scale-free networks
Network control
Network Robustness
Mobility and networks
Social networks and communities
Assortativity of networks
Network stability
Website and Announcements. We will use electronic communication and the course website extensively. You are responsible for checking Submitty regularly for announcements and course materials and your e-mail for communications related to the class.
Lectures. You are responsible for all material covered and announcements made in the class.
Undergraduates (CSCI4250):
One individual programming homework (40% of the total grade). The programming homework will be handed out approximately after the end of the 4th week, together with choice of networks for experiments, and due in three weeks after that. The homework will require using network analytics tools, Gephi (or programming) and analysis of the results obtained for the real and synthetic networks. The graded homework will be returned to undergraduates approximately two weeks after they are handed in.
One individual presentation of the selected research paper (50% of the total grade). Students will choose a topic for research and presentation either from the list of topics associated with the textbook or seminal papers that need to be approved by the instructor in the 7th week. The 25 min in class presentation + 5 min discussions of the assigned topic will be scheduled starting at the end of October.
Participation in discussions for at least two student presentations will provide the remaining (10%) of the total grade.
Graduates (CSCI6250):
Students will choose a topic for research and presentation either from the list of topics
associated with the seminal papers, or from their own current work, if approved by the instructor during the first two weeks of the class.
Around 6th week of the course, the research plan will be due of 3-5 pages defining the project part of the presentation on which research will be based (25%).
The 45 min presentation + 5 min discussion will be due starting at the end of October (40%),
and a written report of 8-12 pages due at the last class (25%).
The remaining 10% of the grade will be assigned based on participation in discussions of the presentations.
Grade ranges: A 96. A- 91, B 85, B- 80, C 70, C- 60, F <60.
Undergraduates (CSCI4250):
The students' performance will be measured using four different methods listed below.
(i) Programming homework
(ii) After selection of a current research paper or textbook problem for a presentation, each student will present the selected paper, including its content, and evaluate its scientific results.
(iii) Contributions to in-class discussions.
The programming homework and presentation plans will measure the student's ability to apply concepts of network science to network analysis.
The presentation slides and evaluation of the paper results will measure student's ability to prepare summary material based on fundamental scientific concepts and basic research.
Graduates (CSCI6250):
Again, students' performance will be measured using three different methods:
(i) After selection of a current research paper, each student will present the selected papers, including its content and evaluate its scientific results.
(ii) Contributions to in-class discussions.
(iii) Independent and novel mini-project on the topic of the presentation using different data, or
methods.
The first two methods are the same as the undergraduate methods (i) and (ii), while the third method assesses students’ ability to apply network science to novel problems.
Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the course structure and content, and teachers must trust that the assignments students turn in are their own. Acts that violate this trust undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities defines various forms of Academic Dishonesty, and all students should familiarize themselves with these. All assignments turned in for a grade in this class must represent the student's work. Submission of any homework that violates this policy will result in a penalty of 0 points for the assignment and failure of the course in case of repetition. Please ask for clarification before preparing or submitting homework if you have any questions concerning this policy. The penalty for not adhering to these academic integrity rules is a failing grade for the assignment on the first offense, then failing the course and potential disciplinary actions by the Institute on any subsequent violations.
The instructor reserves the right to modify this syllabus as deemed necessary at any time during the semester. Emendations to the syllabus will be discussed with students during a class period. Students are responsible for the information given in class. There may also be details about this course not covered in this syllabus.
Do not assume something just because it is not specified in the syllabus. If you are unsure about anything related to the rules guiding this course, consult with the instructor.