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.
Type of Program
- Graduate Program
- Interdisciplinary Degree