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 have the opportunity to work with some of the most prominent network scientists in the world and participate in cutting edge research activities.  With frequent guest lectures, and workshop series, students also have access to experts across disciplines. Through coursework, Institute activities and independent research, students will learn how to design and implement algorithms to characterize network growth, decay, and dynamics; explore the theoretical bases for network processes; navigate large-scale datasets; and gain experience developing network-based inquiries for diverse applications.

The great challenge for network science scholars is that they need to both gain a common foundational training in the fundamental language, methods, and theories of Network Science, while also gaining a theoretical and substantive foundation in a particular discipline to apply these perspectives. 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. While we cannot expect graduate students (or even faculty) to be experts, or even proficient, in all of the tools available, the understanding of, and respect for their potential contributions to novel
interdisciplinary network-based approaches is paramount. In order to provide training for the next generation of network scientists that couples deep disciplinary knowledge with interdisciplinary Network Science, the PhD program provides opportunities to excel in both foundational network science and interdisciplinary problem solving.


In-depth knowledge about disciplinary challenges, processes and constraints is necessary to effectively bridge domain-specific applications with the science of networks. Learning to combine theoretical/substantive questions with the appropriate tools and techniques for data collection and analyses is the key to successful interdisciplinary research. While the research conducted in the institute is wildly flexible in terms of applications, current concentration areas are physics, political science, epidemiology,
and computer and information sciences.

foundational training

The program provides a common, foundational training in all aspects of Network Science (e.g., approaches, languages, problems) beginning in the first year of graduate training.  Current courses offered include Intro to Network Science (taught by Laszlo Barabasi), Dynamical Systems (Alex Vespignani), Network Data (Qian Zhang and Matteo Chinazzi), Social Network Analysis (David Lazer), Data Mining (Tina Eliassi-Rad), Network Economics (Chris Riedl), Bayesian and Network Statistics (Nick Beauchamp) and Graph Theory (Gaboor Lippner).



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 & 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. 

CSS Lab  

The Collaborative Social Systems Lab, directed by Christoph Riedl, explores collaboration in distributed environments: how can individuals solve challenging global tasks in social networks from only local, distributed interactions? We use agent-based modeling, conduct lab and field experiments, and analyze large datasets to study how networked interactions influences human behavior, strategies, and success.

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.

network science Courses    

PHYS 5116 | Complex Networks and Applications

Introduces network science and the set of analytical, numerical, and modeling tools used to understand complex networks emerging in nature and technology. Focuses on the empirical study of real networks, with examples coming from biology (metabolic, protein interaction networks), computer science (World Wide Web, Internet), or social systems (e-mail, friendship networks). Shows the organizing principles that govern the emergence of networks and the set of tools necessary to characterize and model them. Covers elements of graph theory, statistical physics, biology, and social science as they pertain to the understanding of complex systems.
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PHYS 7331 | Network Science Data I

The class is an introductory course on programming for Network and Data Scientists. In this course, students will learn the fundamentals of computer programming (e.g. control structures, data structures, algorithms, ..) with particular focus on applications to Network and Data Science.

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PHYS 7332 | Network Science Data II

Offers an overview of data mining and analysis and techniques in network science. Introduces students to network data analysis. Presents algorithms for the characterization and measurement of networks (centrality based, decomposition, community analysis, etc.) and issues in sampling and statistical biases. Reviews visualization algorithms and specific software tools. Offers students an opportunity to learn about working with real-world network datasets.

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PHYS 7335 | Dynamical Processes in Complex Networks

Immerses students in the modeling of dynamical processes (contagion, diffusion, routing, consensus formation, etc.) in complex networks. Includes 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 course reviews the recent progress in modeling dynamical processes that integrates the complex features and heterogeneities of real-world systems. 

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PHYS 7337 | Statistical Physics of Complex Networks

Covers applications of statistical physics to network science. Focuses on maximum-entropy ensembles of networks, and on applicability of network models to real networks. Main covered topics include microcanonical, canonical, and grand canonical ensembles of networks, exponential random graphs, latent variable network models, graphons, random geometric graphs and other geometric network models, and statistical inference methods using these models. Covers applications of maximum-entropy geometric network models to efficient navigation in real networks, link prediction and community structure inference.

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POLS 7334 | Social Networks

Offers an overview of the literature on social networks, with literature from political science, sociology, economics, and physics. Analyzes the underlying topology of networks and how we visualize and analyze network data. Key topics include small-world literature and the spread of information and disease.

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CS 6220 | Data Mining Techniques

Covers various aspects of data mining, including classification, prediction, ensemble methods, association rules, sequence mining, and cluster analysis. The class project involves hands-on practice of mining useful knowledge from a large data set.

NETS 7341 | Network Economics

Covers seminal works in the economics of information and networks, including Akerlof, Arrow, Spence, Stiglitz, and von Hayek. Proceeds through concepts of information, its value, and measurement; search and choice under uncertainty; signaling, screening, and how rational actors use information for private advantage; strategy-given network effects; two-sided (or multisided) network effects, organizational information processing, learning, and social networks; and other micro- and macroeconomic effects such as matching markets. Although primarily a theory course, it may be of interest to any student applying information economics and network economics in academic, commercial, or government policy contexts. Expects students to produce a major paper suitable for publication or inclusion in a thesis.

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NETS 7350 | Bayesian and Network Statistics

An introduction to advanced quantitative methods including maximum likelihood, hierarchical models, sampling, and network modeling. Students with some experience in basic econometric methods will learn to estimate and develop models from the probabilistic and Bayesian perspective, and will pursue their own research project along the way, with a particular attention the methodological challenges. The course begins with a review of probability, then examines maximum likelihood methods for estimating regression models with continuous and categorical dependent variables. This is followed by examining a variety of procedures for sampling from posterior distributions, including grid, quadratic, Gibbs, and Metropolis sampling. These methods are then applied to hierarchical modeling and other simple probabilistic models. The course then takes a closer look at the statistical modeling of networks as it has been developed in the social sciences, beginning with the Exponential Random Graph Model (ERGM), and _nishing with the temporal SIENA model.

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DSSH 6301 | Introduction to Computational Statistics

Modern data analysis increasingly faces an embarrassment of riches: abundant and complex data, along with increasingly sophisticated techniques for modeling these data and building and testing theories. This course provides an introduction to the fundamental techniques of quantitative data analysis with an emphasis on the diverse skills needed for contemporary work: data acquisition and management, scripting and sampling, probability and statistical tests, econometric models, machine learning, and data visualization. These diverse skills are developed using the open-source R statistical computing language, which has become the dominant statistical tool for modern data analysis.


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 PhD candidate upon completion of all required coursework with a minimum cumulative GPA of 3.0, satisfactory completion of the Qualification Exam, and satisfactory completion of the Comprehensive Exam.

Qualification Examination

The Qualification Exam will be an oral examination of the material during the students’ coursework. The exam will be an hour in length, and consist of questions selected by Network Science faculty who comprise the Qualifying Examination and Dissertation Committee. Students will receive 50 – 80 potential questions, which they must be prepared to answer, one month before the exam. The exam will consist of a subset of these questions. The Qualifying Exam is will be offered twice annually, in the fall and spring term. All students are required to initially sit for the exam in the fall, typically in their third year of the PhD program. Students who do not pass the Qualifying Exam on their first attempt are expected to retake the exam in the spring term.  Students may sit for the Qualifying exam no more than twice.

Students who fail to complete the Qualifying Examination but who have completed all the PhD program’s required course work with a cumulative GPA of 3.000 or better will be awarded a terminal Master of Science in Network Science degree. Note that no students will be admitted directly into the network science program for receipt of a masterʼs degree.

Comprehensive Exam

Students must submit a written dissertation proposal to the Qualifying Examination and Dissertation Committee. The proposal should identify relevant literature, the research problem, the research plan, and the 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 Qualifying Examination and Dissertation Committee. The Comprehensive Exam must precede the final dissertation defense by at least one year.

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 welcome applicants from talented students from all countries around the world with strong interdisciplinary interests. If you have any questions about immigration issues, please feel free to contact us. View President Aoun's statement on embracing our global community here.

The priority application deadline for Fall 2019 is January 1, 2019. Applications received after January 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 January 1 for a Fall start.


Network Science Institute Funding for Scholarly Travel: 
PhD students may apply for funding to cover travel costs and conference/tuition fees. All applications must be reviewed and approved by the Director of the Network Science Institute or their designee prior to funds being awarded. The amount available is subject to change and will be reviewed annually. In order to be eligible for funding, students must:

1) Successfully complete at least two semesters in the program, with a minimum cumulative GPA of 3.0.
2) Receive an endorsement from at least 1 Network Science affiliated faculty member in support of the travel/conference.
3) Write a brief (1 -2 paragraph) personal statement addressing why they wish to attend the event, and how they feel it will benefit them in the PhD program. 
4) Received Network Science Institute Funding for Scholarly Travel no less than 12 months prior.

Degree distribution, rank-size distribution, and leadership persistence in mediation-driven attachment networks
Md. Kamrul Hassana, Liana Islama, Syed Arefinul Haque
Physica A Volume 469, 1 March 2017, Pages 23–30 [view pdf]
Apply Online

Mark Giannini

PhD Program Coordinator

 617 373 8856