The Network Science PhD program provides rigorous, interdisciplinary study in network theory and modeling that combines foundations in physics, computer science, and social sciences. The program prioritizes student flexibility, and you’ll work closely with your advisor and faculty to select courses that match your goals. Throughout the coursework, you will focus on:
- Common applications of network science, including epidemiology, brain science, political science, ecology, organizations, and data mining.
- Basic data visualization techniques to present network findings in a compelling and accurate way.
- Building and analyzing networks using control structures, data structures, and algorithms.
Curriculum By Year
Core courses are primarily taught by faculty from the Network Science Institute. Students typically complete all required courses in their first two years, followed by qualifying and comprehensive exams in their third year. Below is a sample five-year plan of study:
- Fall semester core courses in complex networks and applications, network science data, and network science literature review.
- Spring semester core courses in network science data, social network analysis, and data mining.
- Fall semester core course in dynamical processes in complex networks and research.
- Three electives to support further research.
- Qualifying exam completed.
- Comprehensive exam completed.
YEARS 4 & 5
Explore program courses and requirements.
A student is considered a PhD degree candidate upon:
- Completion of all required coursework with a minimum cumulative 3.0 GPA.
- Satisfactory completion of qualifying examination and comprehensive examination.
The qualifying exam is conducted by the Network Science Graduate Program Committee to test a student’s knowledge and fluency of the core course material.
A PhD student must submit a written dissertation proposal to the dissertation committee. The proposal should identify the research problem, the research plan, and its potential impact on the field. A presentation of the proposal will be made in an open forum, and the student must successfully defend it before the dissertation committee.
A PhD student’s research work culminates in a dissertation, which is defended as a public presentation followed by a closed evaluation with the dissertation committee.
Students are required to complete a total of 38 units of core and elective courses for the PhD in Network Science. The following seven core courses (26 SH) are taken by all students:
PHYS 5116 – Complex Networks and Applications (Fall, 4 units)
PHYS 7331 – Network Science Data I (Fall, 4 units)
PHYS 7335 – Dynamical Processes in Complex Networks (Fall, 4 units)
NETS 8941 – Network Science Literature Review (Fall, 2 units)
POLS 7334 – Social Networks Analysis (Spring, 4 units)
CS 6220 – Data Mining Techniques (Spring, 4 units)
PHYS 7332 – Network Science Data II (Spring, 4 units)
Additionally, students devote half of their time to research activities with one course of directed independent research activities (4 SH), and two courses of dissertation study (8 SH). The directed study course offers students a chance to formally consider a faculty member as a potential advisor.
Explore program courses and requirements.
While the elective choices are not limited, students typically work with their advisor to select courses that will be most useful for building skills in their chosen research area. Common electives include:
CS 5800 – Algorithms
CS 7260 – Visualization for Network Science
DSSH 6301 – Introduction to Computational Statistics
MATH 7233 – Graph Theory
NETS 7341 – Network Economics
NETS 7345 – The Practice of Interdisciplinary Scholarship
NETS 7350 – Bayesian and Network Statistics
PHYS 7337 – Statistical Physics of Complex Networks
The PhD in Network Science is an interdisciplinary program that provides conceptual and mathematical tools to describe and predict networks. Students will demonstrate a graduate-level understanding of foundational network science concepts, including:
- Comprehension of the mathematics of networks, and their applications to biology, sociology, technology, and other fields, and their use in the research of real complex systems in nature and man-made systems.
- Essential network data mining techniques from real-world datasets to networks.
- Statistical descriptors of networks and statistical biases.
- Measures and metrics of networks.
- Network clustering techniques.
- Network modeling.
- Understanding process modeling on networks.
- Network visualization.
- Familiarity with the ongoing research in the field of network science.
Additionally, students will demonstrate a graduate-level understanding of non-network methods that enable network research, including:
- Computational statistics (e.g., for social science track, a wide array of inferential methods).
- Data acquisition and handling.
- Measurement and research design.
Students will also attain a deep understanding of other substantive domains complementary to network science, such as physics, political science, and computer science, and are expected to communicate network science concepts, processes, and results effectively—verbally and in writing—to prepare for potential careers that include industrial research positions, government consultants, and post-doctoral or junior faculty positions in academic institutions.