Course Summary

Networks are everywhere! Networks (graphs) can be used to elegantly model and analyze real-world phenomena in various domains. Examples are the Internet, Facebook, cell phone communications, airline routes, stock markets, disease spread, brain, molecular interactions between genes/proteins in a cell etc. Networks are important! For example, both graphite and diamond are composed of carbon atoms, but what gives them different properties (graphite being soft and dark, diamond being hard and clear) is the connections (links) between the atoms, i.e., the network. So, what is network science about? This interdisciplinary course will introduce students to the different types of real-world networks. Also, the course will introduce state-of-the-art computational approaches for network analysis. Because of the increasing complexity of the real-world network data, in order to analyze the networks efficiently, these approaches will span many fields, e.g., algorithms, graph theory, data mining, machine learning, pattern recognition, information theory, big data, probability and statistics, and visualization. Vibrant network science topics that will be explored include: network properties and measures of network structure, network modeling, network evolution (i.e., dynamic network analysis), link prediction, community detection (i.e., clustering), network comparison and alignment, network integration (i.e., heterogeneous network analysis), and network visualization. While the course will encompass traditional course activities (lectures, homeworks, and exams), the focus will be on getting practical hands-on experience in analyzing real-world network data through a course project, reading latest research papers on network science and its real-life applications in a variety of domains, and active in-class discussion of the papers.

Course Meeting Time and Location

Lecture time: Mondays and Wednesdays, 2–3:15pm

Location: DeBartolo Hall 116


Dr. Tijana Milenkovic

E-mail: tmilenko [at] nd [dot] edu (whenever possible, please use Piazza)

Instructor ofice hours: 3:30-4:30pm on Mondays in 381 Fitzpatrick (Dr. Milenkovic's office)

Piazza: We will aim to respond to any questions that you post within 24 hours


Teaching Assistants (TAs)

Graduate TAs

Salvador Aguinaga

E-mail: saguinag [at] nd [dot] edu (whenever possible, please use Piazza)

Office hours: 12:30-1:30pm on Wednesdays in 384 Nieuwland Hall

Undergraduate TAs

Jacob Kassman

E-mail: Jacob.A.Kassman.1 [at] nd [dot] edu (whenever possible, please use Piazza)

Office hours: By appointment.

Claire Sonderman

E-mail: Claire.A.Sonderman.4 [at] nd [dot] edu (whenever possible, please use Piazza)

Office hours: By appointment.


Course Syllabus

For information about course goals, topics, organization, grading scheme, textbooks and readings, etc., please see the course syllabus.