NETS 8941 - Literature Review Seminar - Spring 2025

Thursdays: 3:30 – 5:15pm
January 9 – April 24, 2025
177 Huntington, room 207

Summary

This Literature Review Seminar is 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 Teaching Professor and Director of the MS Program Complex Network Analysis at Northeastern University. He is the director of the Complexity & Society Lab, which researches two broad topics: 1) Information, emergence, and inference in complex systems — developing tools and theory for characterizing dynamics, structure, and scale in networks, and 2) Public health and public safety — creating and analyzing large scale datasets that reveal inequalities in the United States, from epidemics to mass incarceration. Dr. Klein received a PhD in Network Science in 2020 from Northeastern University and got his BA in Cognitive Science & Psychology from Swarthmore College in 2014. Website: http://brennanklein.com.

Syllabus below (or pdf here).

Week 1: Jan. 9

Introduction to the course and semester goals

Pre-class homework:

  • Please come to class with the following:

    • A list of your favorite papers (between 1 and 5, nothing too substantial) about networks or complex systems. Don't think too much about this! Just be ready to discuss in class.

    • A list of possible scholars who you think would be good guests to invite to the course (either this year, or for future iterations).

Week 2: Jan. 16

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: Jan. 23

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:

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.

Week 4: Jan. 30

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 5: Feb. 6

First principles, scaling, boundaries

Readings:

  • Primary reading: Krakauer, D., Bertschinger, N., Olbrich, E., Flack, J. & Ay, N. (2020). The information theory of individuality. Theory in Biosciences, 139, 209–223. doi: 10.1007/s12064-020-00313-7.

  • Primary reading: Alon, U. (2003). Biological networks: the tinkerer as an engineer. Science, 301(5641), 1866-1867. doi: 10.1126/science.1089072.

  • Primary reading: West, G.B., Brown, J.H., & Enquist, B.J. (1999). The fourth dimension of life: Fractal geometry and allometric scaling of organisms. Science, 284(5420), 1677-1679. doi: 10.1126/science.284.5420.1677.

    • Supplementary reading: West, G.B., Brown, J.H., & Enquist, B.J. (1997). A general model for the origin of allometric scaling laws in biology. Science, 276(5309), 122-126. doi: 10.1126/science.276.5309.122.

    • Supplementary reading: Song, C., Havlin, S. & Makse, H. (2005). Self-similarity of complex networks. Nature, 433, 392–395. doi: 10.1038/nature03248.

    • Supplementary reading: Bettencourt, L.M., Lobo, J., Helbing, D., Kühnert, C., & West, G.B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104(17), 7301-7306. doi: 10.1073/pnas.0610172104.

    • Supplementary reading: Thurner, S., Hanel, R. & Klimek, P. (2018). Introduction to the Theory of Complex Systems: Chapter 1: Introduction. Oxford University Press.

    • Supplementary reading: Thurner, S., Hanel, R. & Klimek, P. (2018). Introduction to the Theory of Complex Systems: Chapter 3: Scaling. Oxford University Press.

    • Supplementary reading: Corominas-Murtra, B., Hanel, R., & Thurner, S. (2015). Understanding scaling through history-dependent processes with collapsing sample space. Proceedings of the National Academy of Sciences, 112(17), 5348-5353. doi: 10.1073/pnas.1420946112.

Additional resources:

  • Levin, M. (2019). The computational boundary of a “self”: Developmental bioelectricity drives multicellularity and scale-free cognition. Frontiers in Psychology, 10, 493866. doi: 10.3389/fpsyg.2019.02688.

Week 6: Feb. 13

Statistics, description, and inference in complex networks

Readings:

  • Primary reading: Peel, L., Peixoto, T.P. & De Domenico, M. (2022). Statistical inference links data and theory in network science. Nature Communications, 13, 6794. doi: 10.1038/s41467-022-34267-9.

  • Primary reading: Peixoto T.P. (2023). Descriptive vs. Inferential Community Detection in Networks: Pitfalls, Myths and Half-Truths. Cambridge: Cambridge University Press. doi: 10.1017/9781009118897.

    • Supplementary reading: Peixoto, T.P. (2023 ed.). Bayesian stochastic blockmodeling. arXiv: 1705.10225 v9. [also published under Peixoto, T.P. (2019). Bayesian Stochastic Blockmodeling. In Advances in Network Clustering and Blockmodeling (eds P. Doreian, V. Batagelj and A. Ferligoj). doi: 10.1002/9781119483298.ch11.]

    • Supplementary reading: Peixoto, T.P. (2019). Network reconstruction and community detection from dynamics. Physical Review Letters, 123(12), 128301. doi: 10.1103/PhysRevLett.123.128301.

    • Supplementary reading: Peixoto, T.P. (2024). Scalable network reconstruction in subquadratic time. arXiv: 2401.01404.

Additional resources:

Week 7: Feb. 20

Network motifs and structure

Readings:

  • Primary reading: Orsini, C., Dankulov, M., Colomer-de-Simón, P., Jamakovic, A., Mahadevan, P., Vahdat, A., Bassler, K., Toroczkai, Z., Boguñá, M., Caldarelli, G., Fortunato, S. & Krioukov, D. (2015). Quantifying randomness in real networks. Nature Communications, 6, 8627. doi: 10.1038/ncomms9627.

  • Primary reading: Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: simple building blocks of complex networks. Science, 298(5594), 824-827. doi: 10.1126/science.298.5594.824.

  • Primary reading: Stamm, F. I., Scholkemper, M., Strohmaier, M., & Schaub, M. T. (2023). Neighborhood structure configuration models. In Proceedings of the ACM Web Conference 2023 (pp. 210-220). doi: 10.1145/3543507.3583266.

    • Supplementary reading: Hočevar, T. & Demšar, J. (2014). A combinatorial approach to graphlet counting. Bioinformatics, 30(4), 559–565. doi: 10.1093/bioinformatics/btt717.

    • Supplementary reading: Karrer, B. & Newman, M. E. (2010). Random graphs containing arbitrary distributions of subgraphs. Physical Review E, 82(6), 066118. doi: 10.1103/PhysRevE.82.066118.

    • Supplementary reading: Stegehuis, C., Hofstad, R.v.d. & van Leeuwaarden, J.S.H. (2019). Variational principle for scale-free network motifs. Scientific Reports, 9, 6762. doi: 10.1038/s41598-019-43050-8.

    • Supplementary reading: Ribeiro, P., Paredes, P., Silva, M. E., Aparicio, D., & Silva, F. (2021). A survey on subgraph counting: Concepts, algorithms, and applications to network motifs and graphlets. ACM Computing Surveys (CSUR), 54(2), 1-36. doi: 10.1145/3433652.

Additional resources:

  • Mahadevan, P., Krioukov, D., Fall, K., & Vahdat, A. (2006). Systematic topology analysis and generation using degree correlations. ACM SIGCOMM Computer Communication Review, 36(4), 135-146. doi: 10.1145/1151659.1159930.

Week 8: Feb. 27

Higher-order, multi-scale, and simple networks

Readings:

  • Primary reading: Benson, A.R., Gleich, D.F., & Leskovec, J. (2016). Higher-order organization of complex networks. Science, 353(6295), 163-166. doi: 10.1126/science.aad9029.

  • Primary reading: Bick, C., Gross, E., Harrington, H. A., & Schaub, M. T. (2023). What are higher-order networks? SIAM Review, 65(3), 686-731. Chicago. doi: 10.1137/21M1414024.

    • Supplementary reading: Torres, L., Blevins, A. S., Bassett, D., & Eliassi-Rad, T. (2021). The why, how, and when of representations for complex systems. SIAM Review, 63(3), 435-485. doi: doi.org/10.1137/20M1355896.

    • Supplementary reading: Boccaletti, S., De Lellis, P., Del Genio, C. I., Alfaro-Bittner, K., Criado, R., Jalan, S., & Romance, M. (2023). The structure and dynamics of networks with higher order interactions. Physics Reports, 1018, 1-64. doi: 10.1016/j.physrep.2023.04.002.

    • Supplementary reading: Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.G. & Petri, G. (2020). Networks beyond pairwise interactions: Structure and dynamics. Physics Reports, 874, 1-92. doi: 10.1016/j.physrep.2020.05.004.

    • Supplementary reading: Klein, B., & Hoel, E. (2020). The emergence of informative higher scales in complex networks. Complexity, 2020, 1-12. doi: 10.1155/2020/8932526.

    • Supplementary reading: Palla, G., Derényi, I., Farkas, I. & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435, 814–818. doi: 10.1038/nature03607.

    • Supplementary reading: Peel, L., Larremore, D. & Clauset, A. (2017). The ground truth about metadata and community detection in networks. Science Advances, 3(5), e1602548. doi: 10.1126/sciadv.1602548.

Additional resources:

  • Neuhäuser, L., Mellor, A., & Lambiotte, R. (2020). Multibody interactions and nonlinear consensus dynamics on networked systems. Physical Review E, 101(3), 032310. doi: 10.1103/PhysRevE.101.032310.

Week 9: Mar. 6

SPRING BREAK NO CLASS

Week 10: Mar. 13

Meaning and measurement in computational social science

Readings:

  • Primary reading: Healy, K. (2017). The performativity of networks. European Journal of Sociology, 56(2), 175-205. doi: 10.1017/S0003975615000107.

  • Primary reading: Lazer, D., Hargittai, E., Freelon, D., González-Bailón, S., Munger, K., Ognyanova, K. & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature 595, 189-196. doi: 10.1038/s41586-021-03660-7.

    • Supplementary reading: Lazer, D., Pentland, A., Watts, D.J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M.J., Strohmaier, M., Vespignani, A. & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060-1062. doi: 10.1126/science.aaz8170.

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

    • Supplementary reading: Dodds, P.S., Alshaabi, T., Fudolig, M.I., Zimmerman, J.W., Lovato, J., Beaulieu, S., Minot, J.R., Arnold, M.V., Reagan A.J. & Danforth, C.M. (2021). Ousiometrics and telegnomics: the essence of meaning conforms to a two-dimensional powerful-weak and dangerous-safe framework with diverse corpora presenting a safety bias. arXiv: 2110.06847.

Week 11: Mar. 20

Explosive phenomena in networks (w/ Michelle Girvan)

Readings:

  • Primary reading: Dimitris, A., D'Souza, R.M., & Spencer, J. (2009). Explosive percolation in random networks. Science 323, no. 5920: 1453-1455. doi: 10.1126/science.1167782.

  • Primary reading: D'Souza, R.M., Gómez-Gardenes, J., Nagler, J., & Arenas, A. (2019). Explosive phenomena in complex networks. Advances in Physics 68, no. 3: 123-223. doi: 10.1080/00018732.2019.1650450.

    • Supplementary reading: Pathak, J., Lu, Z., Hunt, B. R., Girvan, M., & Ott, E. (2017). Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(12). doi: https://doi.org/10.1063/1.5010300.

    • Supplementary reading: Newman, M. E. & Girvan, M. (2003). Mixing patterns and community structure in networks. In Statistical Mechanics of Complex Networks (pp. 66-87). Berlin, Heidelberg: Springer Berlin Heidelberg. doi: https://doi.org/10.1007/978-3-540-44943-0_5.

Guest participant:

Additional resources:

Week 12: Mar. 27

Networks in/of the brain (w/ Rick Betzel)

Readings:

  • Primary reading: Betzel, R.F. & Bassett, D.S. (2018). Specificity and robustness of long-distance connections in weighted, interareal connectomes. Proceedings of the National Academy of Sciences, 115(21), E4880-E4889. doi: 10.1073/pnas.1720186115.

  • Primary reading: Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), e159. doi: 10.1371/journal.pbio.0060159.

    • Supplementary reading: Sporns, O., Tononi, G., & Edelman, G. (2000). Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex 10, 127–141. doi: https://doi.org/10.1093/cercor/10.2.127.

    • Supplementary reading: Bassett, D. & Sporns, O. (2017). Network neuroscience. Nature Neuroscience 20, 353–364. doi: https://doi.org/10.1038/nn.4502.

    • Supplementary reading: Bullmore, E. & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience 13, 336–349. doi: https://doi.org/10.1038/nrn3214.

    • Supplementary reading: Betzel, R.F., Faskowitz, J., & Sporns, O. (2023). Living on the edge: network neuroscience beyond nodes. Trends in Cognitive Sciences, 27(11), 1068-1084. doi: https://doi.org/10.1016/j.tics.2023.08.009.

Guest participant:

Additional resources:

Week 13: Apr. 3

Structural diversity and experimentation (w/ Johan Ugander)

Readings:

  • Primary reading: Ugander, J., Backstrom, L., Marlow, C., & Kleinberg, J. (2012). Structural diversity in social contagion. Proc. Natl. Acad. Sci. U.S.A. 109 (16) 5962-5966. doi: 10.1073/pnas.1116502109.

  • Primary reading: Su, J., Kamath, K., Sharma, A., Ugander, J., & Goel, S. (2020). An Experimental Study of Structural Diversity in Social Networks. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 661-670. doi: 10.1609/icwsm.v14i1.7332.

    • Supplementary reading: Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 329-337). doi: 10.1145/2487575.2487695.

    • Supplementary reading: Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X. (2006). Group formation in large social networks: Membership, growth, and evolution. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, eds Eliassi-Rad, T. Ungar, L.H., Craven, M., Gunopulos, D., pp. 44–54 (2006). doi: 10.1145/1150402.1150412.

    • Supplementary reading: Eckles, D., Karrer, B., & Ugander, J. (2017). Design and analysis of experiments in networks: Reducing bias from interference. Journal of Causal Inference, 5(1), 20150021. doi: 10.1515/jci-2015-0021.

Guest participant:

Additional resources:

  • Backstrom, L., Boldi, P., Rosa, M., Ugander, J., & Vigna, S. (2012). Four degrees of separation. In Proceedings of the 4th annual ACM Web Science Conference (pp. 33-42). doi: 10.1145/2380718.2380723.

  • Dodds, P.S., & Watts, D.J. (2004). Universal behavior in a generalized model of contagion. Physical Review Letters, 92(21), 218701. doi: 10.1103/PhysRevLett.92.218701.

  • Overgoor, J., Benson, A., & Ugander, J. (2019). Choosing to grow a graph: Modeling network formation as discrete choice. In The World Wide Web Conference (pp. 1409-1420). doi: 10.1145/3308558.3313662.

Week 14: Apr. 10

TOPIC TBD (w/ Abigail Jacobs)

Readings:

  • Primary reading: TBD.

    • Supplementary reading: TBD.

Guest participant:

Additional resources:

  • Jacobs, A.Z. (2021). Measurement as governance in and for responsible AI. arXiv. https://arxiv.org/abs/2109.05658.

  • Jacobs, A.Z., & Wallach, H. (2021). Measurement and fairness. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 375-385). doi: 10.1145/3442188.3445901.

Week 15: Apr. 17

Modeling complex and collective action (w/ Juniper Lovato)

Readings:

  • Primary reading: Ostrom, Elinor (2000). Collective Action and the Evolution of Social Norms. Journal of Economic Perspectives 14 (3): 137–158. doi: 10.1257/jep.14.3.137.

    • 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: Epstein, J. (2008). Why Model? SFI Working Paper Series.

Guest participant:

Additional resources:

  • TBD.

Week 16: Apr. 24

Class Postponed

Week 17: May 1

(Bonus week!) TOPIC TBD (w/ James Gleeson)

Readings:

  • Primary reading: TBD.

    • Supplementary reading: TBD.

Guest participant:

Additional resources:

  • TBD.