Network Science: Methods and Applications
Instructor and classroom
Instructor: Constantine Dovrolis
Lectures: Tuesday and Thursday 12:00-1:15
Classroom: Molecular Science and Engineering 1224
Instructor's Office: CODA S1215
Office hours: Tuesday and Thursday, 3:15-4:00 (location: Starbucks coffee shop at Clough building)
Instructor's email: email@example.com
Teaching assistant: (cannot be disclosed here -- see announcement on Canvas)
Office hours: (cannot be disclosed here -- see Canvas)
TA's email: (cannot be disclosed here -- see Canvas)
Lots of valuable information and links
Please do not hesitate to ask for help if you ever feel helpless.
It is often the case that complex systems, both living and man-made,
can be represented as static or dynamic networks of many interacting
components. These components are typically much simpler in terms of behavior
or function than the overall system, implying that the additional complexity
of the latter is an emergent network property.
Network science is a relatively new discipline that investigates the
topology and dynamics of such complex networks, aiming to better
understand the behavior, function and properties of the underlying systems.
The applications of network science cover physical, informational,
biological, cognitive, and social systems.
In this course, we will study algorithmic,
computational, and statistical methods of network science, as well as
applications in communications, biology, ecology, brain science,
sociology and economics.
The course will go beyond the strictly structural concepts of
small-world and scale-free networks, focusing on dynamic network processes
such as epidemics, synchronization, or adaptive network formation.
The course hopes to attract students from different
academic backgrounds and research interests (including
math, physics, engineering, biology, neuroscience or sociology). Consequently, the
instructor will try to keep the course as ``self-contained'' as possible.
However, some knowledge (at the level of a good undergrad course) with calculus, probability, linear algebra, and programming is necessary.
Additionally, students will be free to
choose course projects that are closer to their background.
You cannot take this course for "Audit".
Together with several research papers, we will cover specific chapters from the following three textbooks:
- A-L. Barabási ,
Network Science ,
available online, 2015.
- M.E.J. Newman,
Networks - An introduction ,
Oxford Univ Press, 2010.
- D. Easley and J. Kleinberg,
Networks, Crowds and Markets ,
Cambridge Univ Press, 2010 (also available online).
The following books will be useful references in certain parts of the course:
- R. Cohen and S. Havlin,
Complex Networks - Structure, Robustness and Function ,
Cambridge Univ Press, 2010.
- M.O. Jackson,
Social and Economic Networks ,
Princeton Univ Press, 2008.
- A. Barrat, M. Barthelemy and A. Vespignani,
Dynamical Processes on Complex Networks,
Cambridge Univ Press, 2008.
- E. Kolaczyk,
Statistical analysis of network data,
- S. Wasserman, K. Faust,
Social Network Analysis: Methods and Applications,
Cambridge Univ Press, 1994.
- P. Van Mieghem,
Graph Spectra for Complex Networks,
Cambridge Univ Press, 2011.
- R. Diestel,
Graph Theory (4th edition),
- R.K.Ahuja and T.L.Magnanti,
Network Flows: Theory, Algorithms, and Application ,
The course will consist of three parts:
1. The first part will cover mostly static networks: concepts, metrics, algorithms,
and analytical methods to study complex (not evolving) networks.
2. The second part will cover mostly dynamic networks and/or dynamic processes on networks. In this
case the network either changes with time (through growth, rewiring, etc) and/or a dynamic process is taking place
on the network, changing the state of each node as a function of its neighbors.
3. The third part will focus on applications of network science
from neuroscience, biology, social networks, Internet, etc. We will choose topics and research papers based on
the students' interests and background.
- PART-1: STATIC NETWORKS (4-5 weeks)
- Graph concepts, metrics and basic network models
- Paths, components, degree distribution, degree correlations, clustering
- Various centrality metrics
- Metrics for signed, weighted and spatial networks
- Spectral metrics
- Basic network models (e.g., ER-graphs)
- Properties of many real networks
- Small-world property
- Scale-free property and heavy-tailed degree distributions
- Hierarchy - Modularity
- Core-periphery structure and bow-tie (or hourglass) networks
- Network motifs
- Community detection methods
- Graph partitioning
- Modularity maximization methods
- Hierarchical divisive and agglomerative methods
- Overlapping communities, dynamic communities, and other variations
- Properties of communities in real-world networks
- Statistical (or machine learning) methods in network science - network inference methods
- Network sampling methods
- Learning a network model from data
- Network inference based on cross-correlations
- Inferring causal interactions
- Part-2: DYNAMIC NETWORKS and DYNAMICS ON NETWORKS (5-6 weeks)
- Models of dynamic networks
- Watts-Strogatz model
- Preferential attachment and its many variants
- Kleinberg's duplication-based model
- Optimization-based network formation models (e.g., HOT)
- Dynamics of networks
- Percolation and network resilience to random and targeted attacks
- Dynamics on networks: epidemics and other spreading phenomena
- Network epidemics and epidemic threshold (SI, SIS, SIR models)
- Immunization strategies
- Identification of major spreaders
- Computational network epidemiology
- Dynamics on networks: social influence and cascades
- Social networks and influence/contagion
- The Linear threshold model and the Independent cascades model
- Empirical studies in information and behavior spreading
- Seeding strategies on how to maximize influence
- Dynamics on networks: synchronization, control and more advanced topics
- Synchronization on networks
- Controling networks
- Coevolutionary dynamics (if time allows)
- Interdependent and layered networks (if time allows)
- Temporal networks (if time allows)
- Games on networks (if time allows)
- Network formation games (if time allows)
- Part-3: APPLICATIONS OF NETWORK SCIENCE (2-3 weeks)
- Presentation and discussion of research topics from different disciplines, driven by the students' interests.
Course projects can be of different types:
One option is that you select a research paper that you are very interested in and try to reproduce certain resulst in that paper -- using either the same dataset or with a different dataset.
Another option is that you attempt to study an original research question. In this case you will need to be careful not to propose something that would normally take much longer than 4-5 weeks.
A third option is to implement one or more network analysis algorithms proposed in the literature and evaluate them in a new context.
What kind of project would not be acceptable? Anything that does not really relate to this course. Anything that is too similar to other projects done by the student in his/her research or other courses. Anything that seems too easy or too hard (to the instructor at least).
Groups of two students are ideal but individual projects are also acceptable. Larger teams will
need instructor approval.
All projects will be presented as posters during Finals Week.
- October 11: project proposal
- November 8: progress report
- December 4: final paper due
- TBA: project presentations (poster session)
- Weekly in-class quizzes : 20%
- Homeworks (probably four of them): 50%
- Project (paper and poster presentation): 30%
There are dozens of sites that provide pointers to network datasets.
The following is just a small subset:
The following pointers provide network analysis tools
that you can use in course projects: