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