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: firstname.lastname@example.org 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. I am a co-PI of Confirm, the Science Foundation Ireland Research Centre for Smart Manufacturing.
· Newly published: A. Kartun-Giles, D. Krioukov, J.P. Gleeson, Y. Moreno, G. Bianconi, “Sparse power-law network model for reliable statistical predictions based on sampled data”, Entropy 20(4), 257 (2018); arXiv:1803.00976.
· Appeared in Nature Communications: Gleeson JP and Durrett R, “Temporal profiles of avalanches on networks”, Nature Communications, 8:1227 (2017) open access; arXiv:1612.06477. The simulation codes and network data used in our paper can be downloaded from here.
· New preprint, with Peter Fennell: Peter G. Fennell and James P. Gleeson, “Multistate dynamical processes on networks: Analysis through degree-based approximation frameworks”, arXiv:1709.09969
· Appeared in Physical Review Letters: Tomokatsu Onaga, James P. Gleeson, Naoki Masuda, “Concurrency-induced transitions in epidemic dynamics on temporal networks”, Phys. Rev. Lett. 119, 108301 (2017); arXiv:1702.05054
· Our work is mentioned in this article in Scientific American: “How Fake News Goes Viral—Here’s the Math”.
· Appeared in Royal Society Open Science: David J.P. O'Sullivan, Guillermo Garduño-Hernández, James P. Gleeson, Mariano Beguerisse-Díaz, “Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum”, R. Soc. Open Sci, 4, 170154 (2017) open access; arXiv:1701.00289
· Appeared in Physical Review Letters: Michele Starnini, James P. Gleeson, Marián Boguñá, “Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes”, Phys. Rev. Lett. 118, 128301 (2017); arXiv:1701.02805
· New preprint with Mason Porter (this is the pre-publication version of a chapter for the forthcoming Springer Nature book “Spreading Dynamics in Social Systems”, edited by Sune Lehmann and Yong-Yeol Ahn): James P. Gleeson and Mason A. Porter, “Message-passing methods for complex contagions”, arXiv:1703.08046
· I have been appointed to the Editorial Board of Physical Review E for 2017-2019.
· Appeared in PLoS ONE: Hurd TR, Gleeson JP and Melnik S,”A framework for analyzing contagion in assortative banking networks”, PLoS ONE 12(2), e0170579 (2017) open access.
· Our model (with Yamir Moreno’s group) for viral spreading on social networks that disentangles how human memory times, network structure and competition affect meme popularity has appeared in Physical Review X: Gleeson JP, O’Sullivan KP, Baños RA, Moreno Y, “The effects of network structure, competition and memory time on social spreading phenomena” (title changed from “Determinants of meme popularity”), Phys. Rev. X. 6, 021019 (2016) open access; arXiv:1501.05956. Data used for the paper can be downloaded from here.
· Mason Porter and I have co-authored a book that is now published by Springer: Porter MA and Gleeson JP, “Dynamical Systems on Networks: A Tutorial”, Springer, 2016: ISBN 978-3-319-26641-1 and ISBN 978-3-319-26640-4
· Our paper with Alex Arenas’s group on bond percolation on multiplex networks has appeared in Physical Review X: Hackett A, Cellai D, Gómez S, Arenas A, Gleeson JP, “Bond percolation on multiplex networks”, Phys. Rev. X, 6, 021002 (2016) [open access]; arXiv:1509.09132
· Peter Fennell (a former PhD student from our group) has been awarded a postdoctoral fellowship (one of only 9 international awards) by the James S McDonnell Foundation, which will fund his postdoctoral work in Kristina Lerman’s group at the University of Southern California.
· Our paper on the competition between Facebook apps is now open access 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
· 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.
· Course director MSc in Mathematical Modelling
· MS6011: Advanced Methods I
· MB4005: Analysis
· MS4028: Stochastic differential equations for finance
Recent and upcoming presentations:
· Invited speaker: CompleNet 2019, Tarragona, Spain, 18-21 Mar 2019
· Invited speaker: Perspectives on Complex Systems workshop, TU Berlin, 17-19 Dec 2018
· Invited seminar: University College Cork, 3 May 2018
· Invited seminar: University of Portsmouth, 28 Feb 2018
· Invited seminar: Centre for Networks and Collective Behaviour, University of Bath, 9 Mar 2017
· Invited seminar: Eugene Wigner Colloquium, TU Berlin, 16 Feb 2017
Codes and data:
· Data for Gleeson JP, O’Sullivan KP, Baños RA, Moreno Y, “The effects of network structure, competition and memory time on social spreading phenomena” (title changed from “Determinants of meme popularity”) arXiv:1501.05956 can be downloaded from here.
· 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.
· Stochastic models of popularity on networks
We are developing models for the diffusion of information (“memes”) or choices among multiple items, in the context of online social networks such as Facebook and Twitter.
1. Gleeson JP, O’Sullivan KP, Baños RA, Moreno Y, The effects of network structure, competition and memory time on social spreading phenomena (title changed from “Determinants of meme popularity”), arXiv:1501.05956. Data used for the paper can be downloaded from here.
2. 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.
3. Gleeson JP, Cellai D, Onnela J-P, Porter MA, Reed-Tsochas F, A simple generative model of collective online behaviour, Proc. Nat. Acad. Sci. USA, 111, 10411-10415 (2014) (open access)
· Complex networks: models of structure and dynamics
We have developed methods for analytically calculating the expected size of cascades on random networks, and on networks with clustering (transitivity) and modular structures. Recently we extended these methods to a general class of binary-state dynamics. We have also investigated why mean-field theory often works well, even on highly-clustered networks, and we are interested in generalizing results to multiplex networks.
7. O’Sullivan DJP, O’Keeffe GJ, Fennell PG, Gleeson JP, Mathematical modeling of complex contagion on clustered networks, Front. Phys. 3:71 (2015) (open access) [invited paper for research topic: lessons and challenges in Computational Social Science]
10. Porter MA and Gleeson JP, “Dynamical Systems on Networks: A Tutorial”, Springer, 2016: ISBN 978-3-319-26641-1 and ISBN 978-3-319-26640-4
(an early version is available at arXiv:1403.7663)
12. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA, Multilayer networks, Journal of Complex Networks, 2, 203 (2014) (open access)
13. 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.
16. 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).
· 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.
26. Hurd TR, Gleeson JP and Melnik S, A framework for analyzing contagion in assortative banking networks; arXiv:1610.03936
27. 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.
28. Farrell N, Devine M, Lee W, Gleeson JP, Lyons S, Specifying An Efficient Renewable Energy Feed-in Tariff, MPRA preprint 49777
29. Devine MT, Gleeson JP, Kinsella J, Ramsey DM, A rolling optimisation model of the UK gas market, Networks and Spatial Economics, 1 (2014).
30. 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]
31. Gleeson JP and O’Doherty F, Non-Lorentzian spectral lineshapes near a Hopf bifurcation, SIAM J. Appl. Math., 66, 1669-1688 (2006) [PDF]
32. Lanyon YH et al., Fabrication of nanopore array electrodes by focused ion beam milling, Anal. Chem., 79, 3048 (2007) [PDF]
33. 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]
34. Gleeson JP, Transient micromixing: Examples of laminar and chaotic stirring, Phys. Fluids, 17, 100614 (2005) [PDF]
35. Gleeson JP, Roche OM, West J, and Gelb A, Modelling annular micromixers, SIAM J. Appl. Math., 64, 1294-1310 (2004) [PDF]
· Industry partners
Are part of MACSI, we work with many companies to apply mathematics to solve real-world problems. Examples of recent industry collaborators include Idiro Analytics, Quaternion Risk Management, and Twitter analytics companies ZenLikeFocus and Sinnia.