NETS 8941 - Literature Review Seminar - Fall 2026

Thursdays: 3:30 – 5:15pm
September 10 – December 10, 2026
101 Belvidere, 4th floor BV 134

Summary

This Literature Review Seminar is a course designed to introduce Network Science students to a wide range of foundational research in Network Science and Complex Systems, both contemporary and historical. The goal for students is to leave the course with exposure to the ideas, insights, and techniques that were integral in the creation of Network Science as we know it today. It is difficult to commit rigidly to a single syllabus for this course; as such, the schedule is designed to be edited, expanded upon, and reconsidered. The ultimate goal is not about identifying and mastering a small number of important scientific contributions—instead, this class provides a space to learn about the insights behind the ideas we use today, untangling where and how these ideas came about, and what they evolved into.

This course is open to members of the Network Science Institute community. It is modeled after an informal journal club that was hosted by Professor Alessandro Vespignani from 2016-2018, when many NetSI members would sit together on Friday afternoons to discuss a paper. This would attract students, postdocs, and faculty, all sitting together listening to each others' questions and insights as peers. As the instructor, I will aim to guide discussion and bring students' voices and questions into the conversation, while also being willing to explore tangents and balancing our various expertises.

We also will be inviting guest participants to class. These will typically be more senior researchers who select the article(s) to read and participate in the journal club for that week, essentially as a peer—asking questions, bringing up discussion points, adding context, commenting on other students' ideas, etc. The idea is not necessarily for the students to hear a lecture from the guest participant, but rather to feel what it's like to sit around the same table and discuss big ideas. The guest participants are asked to choose the week's reading(s), which can be about their own work, or ideas that are inspiring their current work, or ideas inspired them as a student, research that they think should be required reading for young network scientists, any/all/none of the above, etc.

Coursework, Class Structure, Grading

This is a weekly discussion-based class. As is the case in typical “journal club” settings, there will naturally be some students who are more interested and invested in the week's readings and will likely participate more. I hope to assign readings that are broad enough that every student has at least one week where the readings are especially salient. At the same time, I challenge every student to come to class prepared to ask questions and share their thoughts about the week's readings, no matter the topic.

Instructor

Brennan Klein is an Assistant Professor in the Department of Communication Studies and the Department of Physics at Northeastern University, and core faculty at the Network Science Institute. He is the program director of the interdisciplinary MS in Complex Network Analysis. Prof. Klein is also the director of the Complexity & Society Lab, which is focused on two broad research areas: 1) Information, emergence, and communication in complex systems: developing tools and theory for characterizing dynamics, structure, and scale in networks, and 2) Data justice: drawing on complex systems science to document—and fight against—emergent or systemic disparities in society, especially as they relate to the U.S. criminal-legal system. He is also the director of NetSI Sport, an interdisciplinary research group studying team coordination, performance, and networks in elite sports. In 2023, Prof. Klein was awarded the René Thom Young Researcher Award, given to a researcher to recognize substantial early career contributions and leadership in research in Complex Systems-related fields. Prof. Klein is the Data for Justice Fellow at the Institute on Policing, Incarceration & Public Safety at Harvard University's Hutchins Center for African & African American Research. He received a PhD in Network Science in 2020 from Northeastern University and earned his BA in Cognitive Science & Psychology from Swarthmore College in 2014.

Syllabus below (or pdf here).

Week 2: Sep. 17

Complexity, old and new

Readings:

• Primary reading: Wheeler, W.M., (1926). Emergent Evolution and the Social. Science, 64(1662), pp. 433-440. doi: https://www.jstor.org/stable/1651238.

• Primary reading: Simon, H. A. (1962). The Architecture of Complexity. Proceedings of the American Philosophical Society, 106(6), 467–482. doi: http://www.jstor.org/stable/985254.

• Primary reading: Holme, P. (2022). What complexity science is, and why. arXiv: 2201.03762.

  • Supplementary reading: Weaver, W. (1948). Science and Complexity. American Scientist, 36(4), 536–544. doi: http://www.jstor.org/stable/27826254.

  • Supplementary reading: Amaral, L.A.N., Ottino, J.M. (2004). Complex networks. European Physical Journal B 38, 147–162. doi: 10.1140/epjb/e2004-00110-5.

  • Supplementary reading: Ashby, W.R. (1962). Principles of the self-organizing system. In Principles of Self-Organization: Transactions of the University of Illinois Symposium, H. Von Foerster and G.W. Zopf, Jr. (eds.), Pergamon Press: London, UK, pp. 255-278.

Week 3: Sep. 24

Philosophy in/and/of networks

Readings:

• Primary reading: Ross, L.N. (2021). Distinguishing topological and causal explanation. Synthese, 198, 9803-9820. doi: 10.1007/s11229-020-02685-1.

  • Supplementary reading: Bertolero, M. & Bassett., D.S. (2020). On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists. Topics in Cognitive Science, 12, 1272–1293. doi: 10.1111/tops.12504.

  • Supplementary reading: Ross, L.N. (2022). Cascade versus mechanism: The diversity of causal structure in science. The British Journal for the Philosophy of Science, 1. doi: 10.1086/723623.

  • Supplementary reading: Ross, L.N. (2021). Causal Concepts in Biology: How Pathways Differ from Mechanisms and Why It Matters. The British Journal for the Philosophy of Science, 72(1), 131-158. doi: 10.1093/bjps/axy078.

  • Supplementary reading: Chang, H. (2004). Inventing temperature: Measurement and scientific progress: Chapter 5. Oxford University Press.

  • Supplementary reading: Andersen, H. (2014). A field guide to mechanisms: Part I. Philosophy Compass, 9(4), 274-283. doi: 10.1111/phc3.12119.

  • Supplementary reading: Andersen, H. (2014). A field guide to mechanisms: Part II. Philosophy Compass, 9(4), 284-293. doi: 10.1111/phc3.12118.

  • Supplementary reading: Rosenblueth, A. & Wiener, N. (1945). The role of models in science. Philosophy of Science, 12(4), 316-321. doi: 10.1086/286874.

Additional resources:

• Ross, L.N. & Bassett, D.S. (2024). Causation in neuroscience: keeping mechanism meaningful. Nature Reviews Neuroscience. doi: 10.1038/s41583-023-00778-7.

Week 4: Oct. 1

Criticality, chaos, and networks (w/ Alessandro Vespignani)

Readings:

• Primary reading: Bak, P., & Chen, K. (1991). Self-organized criticality. Scientific American, 264(1), 46-53. url: http://www.jstor.org/stable/24936753.

• Primary reading: Bak, P., Tang, C., & Wiesenfeld, K. (1988). Self-organized criticality. Physical Review A, 38(1), 364. doi: 10.1103/PhysRevA.38.364.

  • Supplementary reading: Vespignani, A. & Zapperi, S. (1998). How self-organized criticality works: A unified mean-field picture. Physical Review E, 57(6), 6345. doi: 10.1103/PhysRevE.57.6345.

  • Supplementary reading: Dickman, R., Vespignani, A., & Zapperi, S. (1998). Self-organized criticality as an absorbing-state phase transition. Physical Review E, 57(5), 5095. doi: 10.1103/PhysRevE.57.5095.

  • Supplementary reading: Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters, 59(4), 381. doi: 10.1103/PhysRevLett.59.381.

  • Supplementary reading: Anderson, P. W. (1972). More Is Different: Broken symmetry and the nature of the hierarchical structure of science. Science, 177(4047), 393-396. doi: 10.1126/science.177.4047.393.

  • Supplementary reading: Gell-Mann (1995). What is Complexity? Complexity, 1(1). John Wiley and Sons, Inc.

Guest participant:

• Professor Alessandro Vespignani (Northeastern University)

Additional resources:

• SocSim Python package: BTW model. https://socsim.readthedocs.io/en/latest/BTW.html

  • Manna Model. toppling two grains of sand, but with stochasticity

• Serafino, M., Cimini, G., Maritan, A., Rinaldo, A., Suweis, S., Banavar, J. R., & Caldarelli, G. (2021). True scale-free networks hidden by finite size effects. Proceedings of the National Academy of Sciences, 118(2), e2013825118. doi: 10.1073/pnas.2013825118.

• Watkins, N.W., Pruessner, G., Chapman, S.C. et al. (2016). 25 Years of Self-organized Criticality: Concepts and Controversies. Space Science Review 198, 3–44. doi: 10.1007/s11214-015-0155-x.

• Morin, E. (1992), From the concept of system to the paradigm of complexity. Journal of Social and Evolutionary Systems, 15(4), 371-385. doi: 10.1016/1061-7361(92)90024-8.

• Battiston, S., Puliga, M., Kaushik, R. et al. (2012). DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk. Scientific Reports 2, 541. doi: 10.1038/srep00541.

• Constantinos Tsallis - “Both complexity and beauty are hard to define but easy to identify” 

• Petri, A., Paparo, G., Vespignani, A., Alippi, A., & Costantini, M. (1994). Experimental evidence for critical dynamics in microfracturing processes. Physical Review Letters, 73(25), 3423. doi: 10.1103/PhysRevLett.73.3423.