Current Grant Funding

My research is currently partially funded by the National Science Foundation through the grant NSF DMS - 1830547: Spatio-Temporal Data Analysis with Dynamic Network Models (August, 2018 - July, 2021).

I have also received funding from the National Science Foundation for organizing the Data Institute Conference at the University of San Francsico through the grant NSF DMS - 1841307: The Annual Data Institute Conference (March, 2019).


Below, I list my research publications and preprints according to topic in reverse chronological order.
* graduate students that I mentored. ** graduate students that I co-mentored.
For more information about my code or publications, see: Google Scholar Page or my Github Page

Community Detection and Network Embedding

  • Wilson, J.D., Palowitch, J., Bhamidi, S., and Nobel, A.B. (2017) Community extraction in multilayer networks with heterogeneous community structure. The Journal of Machine Learning Research 18(1), 5458 - 5506. <preprint><code>

  • Wilson, J.D., Wang, S. Mucha, P.J., Bhamidi, S., and Nobel, A.B. (2014) A testing based extraction algorithm for identifying significant communities in networks. The Annals of Applied Statistics 8(3), 1853-1891. <reprint><code>

  • Wilson, J.D., Bhamidi, S., and Nobel, A.B. (2013) Measuring the statistical significance of local connections in directed networks. Neural Information Processing Systems Workshop on Frontiers of Network Analysis: Methods, Models and Applications. <reprint>

  • Wilson, J.D., Baybay, M.* Sankar, R., and Stillman, P. Fast Embedding of Multilayer Networks: An Algorithm and Application to Group fMRI. <preprint> (submitted)

Network Change Detection and Monitoring

  • Wilson, J.D., Stevens, N.T., and Woodall, W.H. Modeling and detecting change in temporal networks via a dynamic degree corrected stochastic block model. (2019) In Press, Quality and Reliability Engineering International. <preprint><code>

  • Wilson, J.D. (2018) Discussion of "Real-time Monitoring of Events Applied to Syndromic Surveillance.'' Quality Engineering. 1 - 6.

  • Sparks, R., and Wilson, J.D. (2018) Monitoring communication outbreaks among an unknown team of actors in dynamic networks. Journal of Quality Technology. 1 - 22. <preprint>

  • Jeske, D., Stevens, N.T., Tartakovsky, A., and Wilson, J.D. (2018) Statistical network surveillance. Wiley StatsRef-Statistics Reference Online. 1 - 12.

  • Jeske, D., Stevens, N.T., Tartakovsky, A., and Wilson, J.D. (2018) Statistical methods for network surveillance. To Appear, Applied Statistics Models in Business and Industry.

  • Woodall, W.H., Zhao, M., Paynabar, K., Sparks, R., and Wilson, J.D. (2017) An overview and perspective on social network monitoring. IISE Transactions 49:3, 354 - 365. <preprint>

Exponential Random graph models

  • Stillman, P.E., Wilson, J.D., Denny, M.J., Desmarais, B.A., Cranmer, S.J., and Lu, Z.L. (2019) A Consistent Organizational Structure Across Multiple Functional Subnetworks of the Human Brain. In Press, NeuroImage.

  • Wilson, J.D., Desmarais, B., Cranmer, S., Denny, M., and Bhamidi, S. (2017) Stochastic weighted graphs: flexible model specification and simulation. Social Networks 49, 37 - 47. <preprint><code>

  • Stillman, P.E., Wilson J.D., Denny, M.J., Desmarais, B., Bhamidi, S., Cranmer, S., and Lu, Z-L (2017) Statistical modeling of the default mode brain network reveals a segregated highway structure. Scientific Reports 7 (1), 11694.

  • Lee, J.**, Li, G., and Wilson J.D. Varying-coefficient models for dynamic networks. <preprint><code> (submitted) (An earlier version won the 2017 ENAR Distinguished Student Paper Award)

Network-Based Analyses and Applications

  • Szekely, E., Pappa, I., Wilson, J.D., Bhamidi, S., Jaddoe, V., Verhulst, H.T., and Shaw, P. (2016) Childhood peer network characteristics: genetic influences and links with early mental health trajectories. Journal of Child Psychology and Psychiatry 57(6), 687 - 694. <reprint>

  • Parker, K.S., Wilson, J.D., Marschall, J., Mucha, P.J., and Henderson, J.P. (2015) Network analysis reveals sex- and antibiotic resistance-associated antivirulence targets in clinical uropathogens. American Chemical Society: Infectious Diseases 1(11), 523 - 532. <preprint>

  • Mackay, J. and Wilson, J.D. A Free Market or a Fixed Market? Network Approaches to Detecting Collusion within Regional Labor Markets. (submitted)

  • Wilson, J.D. and Uminsky, D.T. The power of A/B Testing under Social Interference. (submitted)

Technical Reports

  • MacMillan, K.* and Wilson, J.D. (2017) Topic supervised non-negative matrix factorization. <technical report>