PHD program

The Network Science PhD program is a pioneering interdisciplinary program that provides the tools and concepts aimed at understanding the structure and dynamics of networks arising from the interplay of human behavior, socio-technical infrastructures, information diffusion and biological agents.

Students will be able to work with some of the most prominent network scientists in the world and can participate in cutting edge research activities and work with unique large-scale network datasets.  Students will interact and work with members of the network science community representing a wide range of fields, including computer science, information science, complexity, physics, sociology, communication, organizational behavior, political science, and epidemiology.

Working across traditional boundaries, researchers have begun to accelerate the integration of theoretical ideas and research technologies under the set of ideas and approaches that have recently been referred to as Network Science. Research on network connections among multiple types and levels of “actors” offers a potentially powerful mechanism to understand the workings of complex systems across broad areas of science. This perspective requires novel evaluations of, reconfigurations in, and innovations for standard methods of theorizing, data collection and analysis. In order to provide training for the next generation of network scientists that couples deep disciplinary knowledge with interdisciplinary Network Science, the PhD program is built on the following core principles:

interdisiplinary research

In-depth training in disciplines and programs essential to interdisciplinary research. Current concentrations are focused on the physical sciences (physics); social sciences (political science); health science (epidemiology); and computer and information sciences. 

foundational training

Common, foundational training in all aspects of Network Science (e.g., approaches, languages, problems) beginning in the first year of graduate training to build an inherently interdisciplinary science and the next generation of researchers.

ideas + Techniques

Learning to combine theoretical/ substantive questions with the appropriate tools and techniques for data collection and analyses. A key element will be combining ideas, techniques, and collaborations into the novel interdisciplinary approaches that are paramount to Network Science.


Albert-László Barabási

Nick Beauchamp

James Connolly

Rich DeJordy

Martin Dias

Silvia Dominguez

Tina Eliassi-Rad

Peter Furth

Daniel Kim

Dima Krioukov

David Lazer

Gabor Lippner

Ennio Mingolla

Alan Mislove

Dietmar Offenhuber

Chris Riedl

Milica Stojanovic

Yizhou Sun

Alessandro Vespignani

Brooke Foucault Welles

Christo Wilson

Edmund Yeh

research laboratories

Northeastern has several leading laboratories and centers in Network Science, with dozens of faculty, postdoctoral fellows, visiting faculty, and doctoral students. Labs and centers include:

Center for Complex Network ResearcH

The Center for Complex Network Research (CCNR), directed by Professor Barabási, has a simple objective: think networks. The center’s research focuses on how networks emerge, what they look like, and how they evolve; and how networks impact on understanding of complex systems, with applications ranging from the network of human diseases to controlling complex social, economical, and biological systems.

Laboratory for the Modeling of Biological and Socio-technical Systems

The MOBS laboratory is home to research projects aiming at developing innovative mathematical models and computational tools to better understand large-scale complex networks and systems. The laboratory, directed by Alessandro Vespignani, has joint affiliations with the Department of Physics, the Department of Health Sciences and the College of Computer and Information Sciences at Northeastern University.

Lazer Laboratory

The laboratory’s research is based on the idea that how people and organizations are connected together is critical to understanding the functioning, success and failure of actors and systems and ranges from the very micro (social influence processes within groups), to the very macro (the development of global-wide regulatory regimes).

DK Lab  

DK-Lab research focuses mostly on network theory. Specific topics include network geometry, random (geometric) graphs, causal sets, navigation in networks, and fundamentals of network dynamics. 

NULab for Texts, Maps, and Networks

This Northeastern Center is organized around the dual themes of digital humanities and computational social science.


The curriculum provides students with a strong foundation in network science via four core courses, along with substantive expertise via at least three courses in a concentration, and research experience via two research rotations with network science faculty.

Core Courses    

Complex Networks and Applications

This course provides an overview of theories and analytical approaches in Network Science.The course is an interdisciplinary course, focused on the emerging science of complex networks and their applications. The material includes the mathematics of networks, their applications to biology, sociology, technology and other fields, and their use in the research of real complex systems in nature and in man-made systems. Students will learn about ongoing research in the field, and apply their knowledge in the analysis of network models.

Dynamical Processes on Complex Networks

This course focuses on the modeling of dynamical processes (contagion, diffusion, routing, consensus formation etc.) in complex networks. The course partly consists of guest lectures from local and national experts working in process modeling on networks. Dynamical processes in complex networks provide a rationale for understanding the emerging tipping points and nonlinear properties that often underpin the most interesting characteristics of socio-technical systems. The class reviews the recent progress in modeling dynamical processes that integrates the complex features and heterogeneities of real-world systems.

Network Science Data

This course provides an overview of data mining and analysis and other techniques in Network Science. The course introduces students to network data analysis, including algorithms for the characterization and measurement of networks (centrality, decomposition, community analysis etc.); issues in sampling and statistical biases; visualization algorithms; and software tools. Students will learn about working with real-world network datasets.

Social Network Analysis*

This course provides the basic methodology, techniques and theory developed in the analysis of social networks. The course offers an overview of the research on networks in the social sciences, focusing on the literatures covering social influence, diffusion, and persuasion; social capital, and collective action, drawing from sociology, political science, and economics. Students will learn the tools of analysis — in R, Gephi, and Python — as well as the skills necessary for a social sciences approach to networks, such as causal inference and measurement.

Network Data Mining*

This course provides students with knowledge of specific data mining techniques of large scale information networks and large scale network datasets. The class focuses on network representations, different types of networks (document networks and the web, social networks, microblogging networks), community detection, search and topical locality in information networks, intelligent walks on a graph (smart web crawlers), similarity and link prediction, missing link discovery, node and edge classification, node attribute inference, ranking (HITS, pagerank), diffusion (viral prediction), privacy and re-identification.

* Students will select one of these two core courses, although the other may be taken as part of a concentration.

Courses within concentrations


PHYS 7305 Statistical Physics
PHYS 5318 Principles of Experimental Physics
PHYS 7321 Computational Physics
PHYS 7731 Biological Physics


MATH 7241 Probability I
MATH 7233 Graph Theory
MATH 7375 Topics in Topology
MATH 7733 Readings in Graph Theory


NRSG 5121 Epidemiology and Population Health
PHTH 5202 Epidemiology
PHTH 5224 Social Epidemiology

Political Science

POLS 7200 Perspectives on Social Science Inquiry
POLS 7201 Methods of Analysis
POLS 7202 Quantitative Techniques

Computational Science

CS5800 Algorithms
CS5200 Introduction to database systems
CS6240 Parallel Processing/Map Reducing
CS6220 Data Mining Techniques (Prereq: CS5800 or CS7800)
CS6140 Machine Learning (Prereq: CS5800 or CS7800)


Students will complete the following requirements:

Years 1 – 2

• Two foundational core courses in Network Science (Network Science Theory; Network Science Data)
• Three additional core courses tailored to disciplinary, substantive and individual goals (Social Network Analysis; Dynamical Processes in Complex Networks; Network Data Mining).
• Three courses in the student’s specific track or concentration.
• Two additional advanced research rotations with core faculty of the program.

Years 3 – 5

Students work on individual research projects.

Degree Candidacy

A student is considered a Ph.D. degree candidate upon meeting these conditions:
• Completion of core courses with a minimum GPA of 3.0 overall on the core courses, and
• Completion of the qualifying examination.
Degree candidacy will be awarded by the graduate school corresponding to the student’s home department.

Qualifying Examination

The qualifying examination consists of a two-part exam conducted by a committee of three Network Science Doctoral Program faculty members. The research core of the exam is fulfilled with the acceptance of a high-quality paper to a strong peer-reviewed conference or journal. The technical component of the exam is fulfilled when the student passes the Comprehensive Exam. This shall happen at least six months before the dissertation defense.

Comprehensive Exam

A Ph.D. 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. The Comprehensive exam must precede the final dissertation by at least a six months period.

Dissertation Advising

Each student must have one primary advisor from the Network Science Doctoral Program faculty

Dissertation Committee

The Committee must consist of at least 4 members: the dissertation advisor, one additional Network Science Doctoral program faculty member, one member expert in the specific topic of research, and one faculty member from the department in which the student will obtain their concentration. The dissertation advisor must be a full time member of the Northeastern University faculty.

Dissertation Advising

A Ph.D. student must complete and defend a dissertation that involves original research in Network Science.


We expect applications from talented students from around the world with strong interdisciplinary interests.The priority application deadline for Fall 2017 is February 1, 2017. Applications received after February 1st may be considered, depending on space availability. The following material must be included as part of your application:

• Personal Statement
• Transcripts from all institutions attended
• 3 letters of recommendation
• Proof of English proficiency (TOEFL exam with a minimum score of 100 or a degree earned at an institution where English is the medium of instruction).

The TOEFL and GRE scores should be sent to the College of Science. The institution code for College of Science is 3682.

Financial Support

The financial support typically awarded to qualified applicants includes a tuition waiver, health insurance, and a stipend; for a total award value of approximately $63,000. This award amount is subject to change. Student’s responsibilities for the first year of the program are determined as part of the admission and enrollment process, and can include both teaching and/or research opportunities.


To begin the application process, please go to the application portal. Applications are due February 1 for a Fall start.


For further information, please direct inquiries to Mark Giannini.

An overview of the program and application requirements can be found on this flyer: Call for Applications