Prof. James P. Gleeson
(PhD, Applied Math, Caltech, 1999)
MACSI, Department of Mathematics and Statistics,
University of Limerick, Ireland
Telephone: +353 61 202634 Fax: +353 61 334927
Email: email@example.com Twitter: @gleesonj
Office: B3051, Main Building, UL
My group works on mathematical models for stochastic dynamics, particularly on complex networks. As co-director of MACSI, I am also interested in applying mathematical tools and techniques to solving real-world problems, in collaboration with partners from industry, science and engineering. Our research is funded by Science Foundation Ireland and, as part of the PLEXMATH project, by the FET-Proactive scheme of the European Commission’s FP7.
· Course director MSc in Mathematical Modelling
· MS4028: Stochastic differential equations for finance
· Our paper on the competition between apps on Facebook has appeared in PNAS: Gleeson JP, Cellai D, Onnela J-P, Porter MA, Reed-Tsochas F, A simple generative model of collective online behaviour, Proceedings of the National Academy of Sciences USA, 111, 10411-10415 (2014); arXiv:1305.7440
· I’m an invited speaker at NetSci 2014, Berkeley, 5 June 2014, and at the Satellite on Temporal Networks in Human Dynamics at European Conference on Complex Systems, Lucca, 24 Sep 2014
· We analyse a simple model of information diffusion on Twitter-like networks to show that competition between memes poises the system at criticality: Gleeson JP, Ward JA, O’Sullivan KP, Lee WT, “Competition-induced criticality in a model of meme popularity”, Phys. Rev. Lett. 112, 048701 (2014) ; arXiv:1305.4328. This paper was selected for a Synopsis article in APS Physics. Code for numerically calculating the probability generating functions, and implementing the FFT-based inversion, is available from Kevin O’Sullivan’s webpage.
· I am an Associate Editor of the Journal of Complex Networks (Oxford University Press).
· Octave/MATLAB code for solving the differential equations arising from the approximate master equations, pair approximations, and mean-field theories discussed in [Gleeson JP, Phys. Rev. Letters, 107, 068701 (2011)] and [Gleeson JP, Phys. Rev. X, 3, 021004 (2013)] is now available for download from here. Comments and bug reports are welcome.
Recent and upcoming presentations:
· Invited speaker at the Satellite on Temporal Networks in Human Dynamics at European Conference on Complex Systems, Lucca, 24 Sep 2014
· Invited speaker at NetSci 2014, Berkeley, June 2014.
· Invited seminar: Hamilton Institute, NUI Maynooth, 19 Mar 2014.
· Invited seminar: Bristol Centre for Applied Nonlinear Mathematics, 31 Jan 2014.
· Invited seminar: University of Leeds, 2 Dec 2013.
· Invited speaker at Royal Irish Academy Dublin Talks event, Smock Alley Theatre, 18 Nov 2013.
· Keynote speaker at Contagion ’13: Modelling of Disease Contagion Processes satellite meeting, European Conference on Complex Systems, Barcelona 18 Sep 2013.
· Talk at the Collective Contagion satellite workshop, European Conference on Complex Systems, Barcelona 19 Sep 2013.
· Invited speaker at the Irish Mathematical Society Annual Meeting, Maynooth, 26 Aug 2013.
· Co-organiser (with Mason Porter) of “Cascades on Networks” minisymposium, SIAM Conference on Applications of Dynamical Systems, Snowbird Utah, 23 May 2013.
Research areas and selected publications, see also my ResearcherID page
· Stochastic models of popularity on networks
We are developing models for the diffusion of “memes” or choices among multiple items, in the context of online social networks such as Facebook and Twitter.
o Gleeson JP, Ward JA, O’Sullivan KP, Lee WT, Competition-induced criticality in a model of meme popularity, Phys. Rev. Lett. 112, 048701 (2014) ; arXiv:1305.4328. This paper was selected for a Synopsis article in APS Physics.
o Gleeson JP, Cellai D, Onnela J-P, Porter MA, Reed-Tsochas F, A simple generative model of collective online behaviour, Proceedings of the National Academy of Sciences USA, 111, 10411-10415 (2014); arXiv:1305.7440
· Complex networks: dynamics and structural models
We have developed methods for analytically calculating the expected size of cascades on random networks, and on networks with clustering (transitivity) and modular structures [13-16]. Recently we extended these methods to a general class of binary-state dynamics [1,5-10]. We have also investigated why mean-field theory often works well, even on highly-clustered networks [11,12], and we are interested in generalizing results to multiplex networks [3,4].
1. Fennell PG, Gleeson JP, Cellai D, Analytical approach to the dynamics of facilitated spin models on random networks, arXiv:1405.0195
2. Porter MA and Gleeson JP, Dynamical systems on networks: a tutorial, arXiv:1403.7663
4. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA, Multilayer networks, Journal of Complex Networks, doi:10.1093/comnet/cnu016 (2014); arXiv:1309:7233
5. Gleeson JP, Binary-state dynamics on complex networks: pair approximation and beyond, Phys. Rev. X, 3, 021004 (2013) (open access). Octave/Matlab solvers for the differential equations in this paper are available for download from here.
8. Durrett R, Gleeson JP, Lloyd AL, Mucha PJ, Shi F, Sivakoff D, Socolar JES and Varghese C, Graph fission in an evolving voter model, Proc. Natl. Acad. Sci. USA, 109, 3682 (2012) (open access).
13. Melnik S, Porter MA, Mucha PJ, Gleeson JP, Dynamics on modular networks with heterogeneous correlations, arXiv:1207.1809 (2012)
· Systemic risk models for contagion in banking networks
We examine how the topology of banking networks can lead to system-wide contagion, using a variety of models for bank default.
18. Hurd TR and Gleeson JP, A framework for analyzing contagion in banking networks, submitted; arXiv:1110.4312
19. Gleeson JP, Hurd TR, Melnik S, Hackett A, Systemic risk in banking networks without Monte Carlo simulation, in Advances in Network Analysis and its Applications, E. Kranakis ed., pp27-56, Springer (2012) PDF.
· Mathematical modelling
Mathematical modelling of stochastic effects, in collaboration with engineers and applied scientists, e.g., energy markets, noise in electronic oscillators, mixing, sorting and diffusion in microfluidic devices.
20. Devine MT, Gleeson JP, Kinsella J, Ramsey DM, A rolling optimisation model of the UK gas market, Networks and Spatial Economics, 1 (2014).
21. O’Doherty F and Gleeson JP, Phase diffusion coefficient for oscillators perturbed by colored noise, IEEE Trans. Circuits and Systems II, 54, 435-439 (2007). [PDF]
22. Gleeson JP and O’Doherty F, Non-Lorentzian spectral lineshapes near a Hopf bifurcation, SIAM J. Appl. Math., 66, 1669-1688 (2006) [PDF]
23. Lanyon YH et al., Fabrication of nanopore array electrodes by focused ion beam milling, Anal. Chem., 79, 3048 (2007) [PDF]
24. Gleeson JP, Sancho JM, Lacasta AM, and Lindenberg K, Analytical approach to sorting in periodic and random potentials, Phys. Rev. E, 73, 041102 (2006) [PDF]
25. Gleeson JP, Transient micromixing: Examples of laminar and chaotic stirring, Phys. Fluids, 17, 100614 (2005) [PDF]
26. Gleeson JP, Roche OM, West J, and Gelb A, Modelling annular micromixers, SIAM J. Appl. Math., 64, 1294-1310 (2004) [PDF]