Meeting information Time: 14:00-15:15am, MR Location: EN-2022 Instructor: Dr. Yuanzhu Chen Outline Complex problems emerging from various disciplines and interaction of large amount of data can be modelled with networks. Networks are powerful tools for making sense of the world in the big-data era. The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest recently. The rise of the Internet, the wide availability of inexpensive computers, and penetration of smart mobile devices have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks. The study of large, complex networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. Subjects covered in this course include the measurement and structure of networks, the fundamentals of network theory, computer algorithms, and spectral methods and mathematical models of networks, all at a much greater scale.
References Network Science by Albert-Laszlo Barabasi, Cambridge University Press, 2016 Networks: an introduction by Mark Newman, Oxford University Press 2010
Evaluation Assignments (10) — 50% Project — 50% (10% proposal, 10% presentation, and 30% written report)
Important university policies Academic Misconduct Accommodations for Students with Disabilities

Attention:

• All assignments and reports must be typeset using LaTeX
• All assignments are due 11:59pm on Sundays

Assignments

1. Assignment 1 (submit part 1 by January 12th)
   1. All questions of Section 1.8: Homework
   2. Teach yourself Cytoscape
2. Assignment 2 (submit parts 1 and 2 by January 19th)
   1. Questions .2, .3, .4, .6 of Section 2.12: Homework
   2. Include a few screenshots of your learning results about Cytoscape
   3. Work through Section 2.13 Advanced Topics on your own
3. Assignment 3 (submit parts 1 and 2 by Feb 2nd)
   1. Questions of Section 3.11: Homework
   • For the case of P3.11.2, include your Python code (or Jupyter Notebook file) and generated network files in the submitted bundle
   2. Reproduce plots of Figures 3.6
   • Use the matplotlib.pyplot package of Python.
   • Don't worry about annotating your plots with text labels if you are pressed by time. (You are certainly welcome if you would like to take up this tiny extra work.)
   3. Work through Sections 3.12-.18 Advanced Topics on your own
4. Assignment 4 (submit everyhing in a single Jupyter Notebook file by Feb 9th)
   1. Questions 4.10.1 and 4.10.2 in Section 4.10: Homework
   2. Reproduce Figure 4.22, Plotting a Degree Distribution, to study different binning techniques
   3. (Hint: the Notebook also accepts LaTeX commands for mathematical notation in the Markdown cells in case you are not already using it.)
5. Assignment 5 (submit by March 1st)
   • Reproduce Figure 5.6 Degree Dynamics
   • Submit everthing a single Jupyter Notebook file
   • Use %matplotlib inline to render your figures inside the notebook
6. Assignment 6 (submit by March 8th)
   1. Review Section 6.6, which summarizes a number of issues we have encountered in this book so far, from the topology and model perspectives.
   2. In the pictorial summary of Figure 6.15, for each box with an equation
      • Find the equation number in the book.
      • Summarize its signficance in a few sentences
   3. Submit Part 2 in a PDF file.
7. Assignment 7 (submit by March 15th)
   1. Reproduce Figure 7.9 Randomization and Degree Correlations
   2. Submit as Jupyter Notebook file
8. Assignment 8 (submit by March 22nd)
   1. Reproduce Figure 8.11 Scale-Free Network Under Attack
   2. Submit as Jupyter Notebook file
9. Assignment 9 (submit by March 31st)
   1. Reproduce Figure 9.36(a,c,d,h) Hierarchy in Real Networks
   2. Submit as Jupyter Notebook file
   3. It should not take a few minutes to run your code. If third party network libraries (e.g. networkx or snappy) are too slow, you may wlso ant to consider your own fast implementation.

Project (Yay!)

Presentation schedule on TBD

Date	Team	Title

(Note: safely skip sections of Advanced Topics in the text unless otherwise instructed.)

• Week 1: Introduction
Tasks
• Study Chapters 0 and 1
• Set up your Python programming environment for data and network analysis
Article readings
• On power-law relationships of the internet topology, Faloutsos, M., Faloutsos, P., and Faloutsos, C., Comput. Commun. Rev. 29, 251-262 (1999).
• Complex systems analysis of series of blackouts: Cascading failure, critical points, and self-organization, Dobson, I., Carreras, B. A., Lynch, V. E., and Newman, D., Chaos 17, 026103 (2007).
• Week 2: Graph theory
Tasks
• Study Chapter 2
• Visualize some of the 10 networks downloaded from the text website
Article readings
• The Anatomy of the Facebook Social Graph. 2012. J. Ugander, B. Karrer, L. Backstrom, C. Marlow.
• The political blogosphere and the 2004 US election, Adamic, L. A. and Glance, N., in Proceedings of the WWW-2005 Workshop on the Weblogging Ecosystem (2005).
• Week 3: Random networks
Tasks
• Study Chapter 3
Article readings
• Collective dynamics of small-world networks. D. J. Watts and S. H. Strogatz. Nature, 393:440-442, 1998.
• Growing and Navigating the Small World Web by Local Content. F. Menczer. Proc. Natl. Acad. Sci., 99(22): 14014-14019, 2002.
• Week 4: Scale-free property
Tasks
• Study Chapter 4
Article readings
• Emergence of scaling in random networks. A.-L. Barabasi and R. Albert. Science, 286:509-512, 1999.
• Navigation in a small world, Kleinberg, J. M., Nature 406, 845 (2000).
• Week 5: Measures and metrics (I)
Tasks
• Study Sections 7.1-7.5 of Newman book
• Week 6: Measures and metrics (II)
Tasks
• Study Sections 7.6-7.13 of Newman book
• Week 7: Barabasi-Albert model
Tasks
• Study Chapter 5
Article readings
• Week 8: Evolving networks
Tasks
• Study Chapter 6 (except Section 6.4)
Article readings
• The History and Meaning of the Journal Impact Factor. Eugene Garfield. JAMA. 2006;295(1):90-93. doi:10.1001/jama.295.1.90
• Quantifying Long-Term Scientific Impact. Dashun Wang, Chaoming Song, Albert-Laszlo Barabasi. Science, 342(6154):127-132, Oct 2013, DOI: 10.1126/science.1237825.
• Week 9: Degree correlations
Tasks
• Study Chapter 7
Article readings
• Generalized friendship paradox in complex networks: The case of scientific collaboration. Eom and Jo. Scientific Reports 4:4603, 2015.
• Week 10: Network robustness
Tasks
• Study Chapter 8
Article readings
• Error and attack tolerance of complex networks. A. Albert, H. Jeong, and A.-L. Barabasi. Nature, 406:378-482, 2000.
• Week 11: Communities
Tasks
• Study Chapter 9
Article readings
• Why social networks are different from other types of networks. Newman and Park. Physical Review E, 68:036112, 2003
• Week 12: Spreading phenomena
Tasks