For a complete listing of publications, refer to ORCID, Scopus, or Google Scholar.

• 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.

4. 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
5. Fennell PG, Melnik S, Gleeson JP, “The limitations of discrete-time approaches to continuous-time contagion dynamics”, Phys. Rev. E, 94, 052125 (2016); arXiv:1603.01132
6. Faqeeh A, Melnik S, Colomer-de-Simón P, Gleeson JP, “Emergence of coexisting percolation clusters in networks”, Phys. Rev. E, 93, 062308 (2016); arXiv:1508.05590
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]
8. Faqeeh A, Melnik S, Gleeson JP, Network cloning unfolds the effect of clustering on dynamical processes, Phys. Rev. E, 91, 052807 (2015); arXiv:1408.1294
9. Fennell PG, Gleeson JP, Cellai D, Analytical approach to the dynamics of facilitated spin models on random networks,  Phys. Rev. E, 90, 032824 (2014); arXiv:1405.0195
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)
11. Cellai D, Lopez E, Zhou J, Gleeson JP, Bianconi G, Percolation in multiplex networks with overlap, Phys. Rev. E, 88, 052811 (2013); arXiv:1307.6359
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.
14. Melnik S, Ward JA, Gleeson JP, Porter MA, Multi-stage complex contagion, Chaos, 23, 013124 (2013);  arXiv:1111.1596
15. Cellai D, Lawlor A, Dawson KA, Gleeson JP, Critical phenomena in heterogeneous k-core percolation, Phys. Rev. E, 87, 022134 (2013); arXiv:1209.2928
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).
17. Gleeson JP, High-accuracy approximation of binary-state dynamics on networks, Phys. Rev. Letters, 107, 068701 (2011); extended version at arXiv:1104.1537
18. Cellai D, Lawlor A, Dawson KA, Gleeson JP, Tricritical point in heterogeneous k-core percolation, Phys. Rev. Letters, 107, 175703 (2011); arXiv:1106.1565
19. Gleeson JP, Melnik S, Ward J, Porter MA, Mucha PJ, Accuracy of mean-field theory for dynamics on real-world networks, Phys. Rev. E, 85, 026106 (2012); arXiv:1011.3710
20. Melnik S, Hackett A, Porter MA, Mucha PJ, Gleeson JP, The unreasonable effectiveness of tree-based theory for networks with clustering, Phys. Rev. E, 83, 036112 (2011); arXiv:1001.1439
21. Melnik S, Porter MA, Mucha PJ, Gleeson JP, Dynamics on modular networks with heterogeneous correlations, Chaos, 24, 023106 (2014); arXiv:1207.1809
22. Hackett A and Gleeson JP, Cascades on clique-based graphs, Phys. Rev. E,87, 062801 (2013); arXiv: 1206.3075
23. Gleeson JP, Bond percolation on a class of clustered random networks, Phys. Rev. E, 80, 036107 (2009), arXiv:0904.4292
24. Gleeson JP, Cascades on correlated and modular random networks, Phys. Rev. E, 77, 046117 (2008); [PDF]

• 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.

25. Hurd TR and Gleeson JP, On Watts’ cascade model with random link weights, Journal of Complex Networks, 1, 25-43 (2013); arXiv:1211.5708
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.

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