James Bagrow

Associate Professor

Mathematics & Statistics and Vermont Complex Systems Center,
Innovation E426, University of Vermont
james.bagrow [at] uvm.edu    Curriculum Vitae

Hey, I'm an editor at Nature Scientific Reports. You should go submit something!
Check out our Masters and PhD in Complex Systems & Data Science Programs!

News

January 2019: Our study of information flow and the predictability of online social data is out now in Nature Human Behaviour. Press coverage in Science, Reuters, Sky News. I was a guest on the Marketplace Tech podcast and the BBC Click! podcast. Even more press coverage!

October 2018: Our CSCW paper on crowdsourcing causal networks received a Best Paper honorable mention!

I am looking for bright and motivated PhD students who are interested in network science, machine learning, crowdsourcing, and more!! Interested? More information

Research

Communities
Human mobility
Robust networks
Dynamics

I'm interested in the underlying rules and organizing principles of complex physical and social systems. My work combines mathematical models with large-scale data analysis to better understand these systems, with a particular emphasis on network science and human dynamics.

Meet the group
github.com/bagrow

Teaching

    • STAT/CS 287: Data Science I
    • [UVM, Fall 2018]
    • STAT/CS 387: Data Science II
    • [UVM, Spring 2018]
    • STAT/CS 287: Data Science I
    • [UVM, Fall 2017]
    • MATH 271: Advanced Engineering Mathematics
    • [UVM, Spring 2017]
    • STAT 387: Data Science II
    • [UVM, Spring 2017]
    • STAT/CS 287: Data Science I
    • [UVM, Fall 2016]
    • STAT 387: Data Science II
    • [UVM, Spring 2016]
    • STAT/CS 287: Data Science I
    • [UVM, Fall 2015]
    • MATH 271: Advanced Engineering Mathematics
    • [UVM, Spring 2015]
    • MATH 295: Data Science II
    • [UVM, Fall 2014]
    • MATH 195: Intro to Data Science and Visualization
    • [UVM, Spring 2014]

Publications

Google Scholar Profile     ORCID iD iconorcid.org/0000-0002-4614-0792

  1. An information-theoretic, all-scales approach to comparing networks,
    J. P. Bagrow and E. M. Bollt, Applied Network Science, 4 (1): 45 (2019)
  2. UAFS: Uncertainty-Aware Feature Selection for Problems with Missing Data,
    A. J. Becker and J. P. Bagrow, Preprint (2019)
  3. Accurate inference of crowdsourcing properties when using efficient allocation strategies,
    A. Hotaling and J. P. Bagrow, Preprint (2019)
  4. Inferring the size of the causal universe: features and fusion of causal attribution networks,
    D. Berenberg and J. P. Bagrow, Preprint (2018)
  5. Autocompletion interfaces make crowd workers slower, but their use promotes response diversity,
    X. Liu and J. P. Bagrow, Human Computation 6:1:42-55 (2019)
  6. Information flow reveals prediction limits in online social activity,
    J. P. Bagrow, X. Liu, and L. Mitchell, Nature Human Behaviour 3: 122-128 (2019)
  7. Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution,
    D. Berenberg and J. P. Bagrow, In Proc. ACM Hum.-Comput. Interact. (CSCW'18) (2018)

    Best Paper honorable mention.

  8. Neural language representations predict outcomes of scientific research,
    J. P. Bagrow, D. Berenberg, and J. Bongard, Preprint (2018)
  9. Crowd ideation of supervised learning problems,
    J. P. Bagrow, Preprint (2018)
  10. Crowdsourcing Predictors of Residential Electric Energy Usage,
    M. D. Wagy, J. C. Bongard, J. P. Bagrow, and P. D. H. Hines, IEEE Systems Journal, 12 (4): 3151-3160 (2018)
  11. The quoter model: a paradigmatic model of the social flow of written information,
    J. P. Bagrow and L. Mitchell, Chaos, 28 (7): 075304 (2018)
  12. Information spreading during emergencies and anomalous events,
    J. P. Bagrow, In S. Lehmann & Y.-Y. Ahn (Eds.), Complex Spreading Phenomena in Social Systems (pp. 269-286). Springer (2018)
  13. Predicting an Effect Event from a New Cause Event Using a Semantic Web Based Abstraction Tree of Past Cause-Effect Event Pairs,
    P. Shrestha, B. S. Lee, and J. P. Bagrow, Proceedings of the 4th Annual International Symposium on Information Management and Big Data (SIMBig) (2017)
  14. Which friends are more popular than you? Contact strength and the friendship paradox in social networks,
    J. P. Bagrow, C. M. Danforth, and L. Mitchell, 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2017)
  15. Reply & Supply: Efficient crowdsourcing when workers do more than answer questions,
    T. C. McAndrew, E. A. Guseva, and J. P. Bagrow, PLOS ONE, 12 (8): e0182662 (2017)
  16. Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence,
    M. Korkali, J. G. Veneman, B. F. Tivnan, J. P. Bagrow and P. D. H. Hines, Nature Scientific Reports, 7: 44499 (2017)
  17. What we write about when we write about causality: Features of causal statements across large-scale social discourse,
    T. C. McAndrew, J. C. Bongard, C. M. Danforth, P. S. Dodds, P. D. H. Hines, and J. P. Bagrow, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
  18. Transitions in climate and energy discourse between Hurricanes Katrina and Sandy,
    E. M. Cody, J. C. Stephens, J. P. Bagrow, P. S. Dodds, and C. M. Danforth, J. Environ. Stud. Sci: 1-15 (2016)
  19. Understanding the group dynamics and success of teams,
    M. Klug and J. P. Bagrow, R. Soc. open sci 3: 160007 (2016)
  20. Zipf’s law is a consequence of coherent language production,
    J. R. Williams, J. P. Bagrow, A. J. Reagan, S. E. Alajajian, C. M. Danforth, and P. S. Dodds, Preprint (2016)
  21. Identifying missing dictionary entries with frequency-conserving context models,
    J. R. Williams, E. M. Clark, J. P. Bagrow, C. M. Danforth, and P. S. Dodds, Phys. Rev. E 92 (4): 042808 (2015)
  22. Zipf's law holds for phrases, not words,
    J. R. Williams, P. R. Lessard, E. M. Clark, S. Desu, J. P. Bagrow, C. M. Danforth, and P. S. Dodds, Nature Scientific Reports, 5: 12209 (2015)
  23. Robustness and modular structure in networks,
    J. P. Bagrow, S. Lehmann and Y.-Y. Ahn, Network Science, 3 (4): 509-525 (2015)
  24. Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristics,
    P. S. Dodds, E. M. Clark, S. Desu, M. R. Frank, A. J. Reagan, J. R. Williams, L. Mitchell, K. D. Harris, I. M. Kloumann, J. P. Bagrow, K. Megerdoomian, M. T. McMahon, B. F. Tivnan, and C. M.Danforth, Proc. Natl. Acad. Sci. U.S.A., (2015)
  25. Text mixing shapes the anatomy of rank-frequency distributions: A modern Zipfian mechanics for natural language,
    J. R. Williams, J. P. Bagrow, C. M. Danforth, P. S. Dodds, Phys. Rev. E 91 (5): 052811 (2015)
  26. Robustness of Spatial Micronetworks,
    T. C. McAndrew, C. M. Danforth, and J. P. Bagrow, Phys. Rev. E, 91: 042813 (2015)
  27. Human language reveals a universal positivity bias,
    P. S. Dodds, E. M. Clark, S. Desu, M. R. Frank, A. J. Reagan, J. R. Williams, L. Mitchell, K. D. Harris, I. M. Kloumann, J. P. Bagrow, K. Megerdoomian, M. T. McMahon, B. F. Tivnan, and C. M.Danforth, Proc. Natl. Acad. Sci. U.S.A. 112 (8): 2389-2394, (2015)
  28. PTSD Symptoms, Disability, and Social Support in the Acute Period after a Traumatic Injury: A Preliminary Investigation of Competing Hypotheses,
    M. Price, M. Evans, and J. P. Bagrow, J Trauma Stress Disor Treat 4:1, (2014)
  29. Constructing a taxonomy of fine-grained human movement and activity motifs through social media,
    M. R. Frank, J. R. Williams, L. Mitchell, J. P. Bagrow, P. S. Dodds, C. M. Danforth, In preparation. (2015)
  30. Social Networks in Emergency Response,
    D. Wang, Y.-R. Lin and J. P. Bagrow, Chapter in Encyclopedia of Social Network Analysis and Mining. (2014)
  31. Quantifying Information Flow During Emergencies,
    L. Gao, C. Song, Z. Gao, A.-L. Barabási, J. P. Bagrow, and D. Wang, Nature Scientific Reports, 4: 3997 (2014)
  32. Shadow networks: Discovering hidden nodes with models of information flow,
    J. P. Bagrow, S. Desu, M. R. Frank, N. Manukyan, L. Mitchell, A. Reagan, E. E. Bloedorn, L. B. Booker, L. K. Branting, M. J. Smith, B. F. Tivnan, C. M. Danforth, P. S. Dodds, and J. C. Bongard, In preparation. (2013)
  33. Robustness of skeletons and salient features in networks,
    L. M. Shekhtman, J. P. Bagrow, and D. Brockmann, Journal of Complex Networks 2 (2): 110-120, (2014)
  34. Eyjafjallajökull and 9/11: The Impact of Large-Scale Disasters on Worldwide Mobility,
    O. Woolley-Meza, D. Grady, C. Thiemann, J. Bagrow, and D. Brockmann, PLOS ONE, 8 (8): e69829 (2013)
  35. The role of caretakers in disease dynamics,
    C. Noble, J. P. Bagrow and D. Brockmann, J. Stat. Phys., 152 (4): 787–798 (2013)
  36. Natural emergence of clusters and bursts in network evolution,
    J. P. Bagrow and D. Brockmann, Phys. Rev. X, 3: 021016 (2013)
  37. Is coaching experience associated with effective use of timeouts in basketball?,
    S. Saavedra, S. Mukherjee and J. P. Bagrow, Nature Scientific Reports, 2: 676 (2012)
  38. Communities and bottlenecks: Trees and treelike networks have high modularity,
    J. P. Bagrow,
    Phys. Rev. E, 85: 066118 (2012)
  39. Mesoscopic Structure and Social Aspects of Human Mobility,
    J. P. Bagrow and Y.-R. Lin, PLOS ONE, 7 (5): e37676 (2012)
  40. Quantifying Bias in Social and Mainstream Media,
    Y.-R. Lin, J. P. Bagrow and D. Lazer, SIGWEB Newsletter (Summer) (2012)
  41. Flavor network and the principles of food pairing,
    Y.-Y. Ahn, S. E. Ahnert, J. P. Bagrow and A.-L. Barabási, Nature Scientific Reports, 1: 196 (2011)

    Note: This paper has been downloaded over 100 thousand times! During the first month it was the most accessed paper from all Nature journals, including Nature itself.

  42. Visualizing relations using the "Observable Representation",
    L. S. Schulman, J. P. Bagrow and B. Gaveau, Advances in Complex Systems, 14 (6): 829-851 (2011)
  43. More Voices than Ever? Quantifying Bias in Social and Mainstream Media,
    Y.-R. Lin, J. P. Bagrow and D. Lazer, In International AAAI Conference on Weblogs and Social Media (2011)
  44. Collective Response of Human Populations to Large-Scale Emergencies,
    J. P. Bagrow, D. Wang and A.-L. Barabási, PLOS ONE, 6 (3): e17680 (2011)
  45. Link communities reveal multiscale complexity in networks,
    Y.-Y. Ahn, J. P. Bagrow and S. Lehmann, Nature, 466: 761-764 (2010)
  46. Investigating Bimodal Clustering in Human Mobility,
    J. P. Bagrow and T. Koren, International Conference on Computational Science and Engineering, 4: 944-947 (2009)
  47. Dynamic Computation of Network Statistics via Updating Schema,
    J. Sun, J. P. Bagrow, E. M. Bollt and J. D. Skufca, Phys. Rev. E, 79: 036116 (2009)
  48. Kleinberg navigation on anisotropic lattices,
    J. M. Campuzano, J. P. Bagrow and D. ben-Avraham, Research Letters in Physics, 2008 (2008)
  49. Phase transition in the rich-get-richer mechanism due to finite-size effects,
    J. P. Bagrow, J. Sun and D. ben-Avraham, J. Phys. A: Math. Theor., 41: 185001 (2008)
  50. Evaluating Local Community Methods in Networks,
    J. P. Bagrow,
    J. Stat. Mech., 2008 (5): P05001 (2008)
  51. Portraits of Complex Networks,
    J. P. Bagrow, E. M. Bollt, J. D. Skufca and D. ben-Avraham, Europhysics Letters, 81: 68004 (2008)
  52. Network Structure Revealed by Short Cycles,
    J. P. Bagrow, E. M. Bollt and L. da F. Costa, (2006)
  53. On the Google-Fame of Scientists and Other Populations,
    J. P. Bagrow and D. ben-Avraham, In Proc. Am. Inst. of Physics Conf., pages 81-89 (2005)
  54. A Local Method for Detecting Communities,
    J. P. Bagrow and E. M. Bollt, Phys. Rev. E, 72: 046108 (2005)
  55. How Famous is a Scientist? -- Famous to Those Who Know Us,
    J. P. Bagrow, H. D. Rozenfeld, E. M. Bollt and D. ben-Avraham, Europhysics Letters, 67: 511 (2004)
  56. Photometric Separation of Physical Properties of Stars,
    M. K. Nordhaus, H. J. Newberg, J. P. Bagrow, C. Rider, D. Tucker, H. A. Rave and J. A. Smith., American Astronomical Society, 201st AAS Meeting, #16.12; Bulletin of the American Astronomical Society, 34: 1126 (2002)