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: james.gleeson@ul.ie Twitter: @gleesonj

Office: B3051,
Main Building, UL

**Research**

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 by the EU.

My Google
Scholar page is here; my publications can also be viewed through my ORCID page, or on Scopus.

**News: **

·
I am seeking a postdoctoral researcher (PhD completed) to join my group,
working on mathematical modelling and/or data analytics for computational
social science. The deadline for applications is 12 noon IST on 1^{st}
March 2018, see the advert for
details.

·
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.

**Teaching:**

·
Course director MSc in
Mathematical Modelling

·
MS6011: Advanced Methods I

·
MB4005: Analysis

·
MS4028: Stochastic differential equations for finance

**Recent and upcoming presentations:**

·
Keynote speaker: BIFI International
Conference on Complexity, Networks and Collective Behaviour, Patio de la Infanta, Zaragoza, Spain, 6-8 Feb 2018

·
Invited seminar: Industrial
Applied Mathematics seminar, Mathematical Institute, University of Oxford, 18
Jan 2018

·
Invited speaker: NetSci-X 2018 International School and Conference on
Network Science, Hangzhou, China, 5-8 Jan 2018

·
Invited speaker: Workshop
on Advances on Epidemics in Complex Networks, Delft University of Technology,
31 Aug-1 Sep 2017

·
Invited seminar: Information Sciences
Institute, University of Southern California, Los Angeles, 7 Jul 2017

·
Invited lecturer at Summer
School on Complex Networks: Theory, Methods and Applications, Lake Como School
of Advanced Studies, 15-19 May 2017

·
Invited seminar: Centre for
Networks and Collective Behaviour, University of Bath, 9 Mar 2017

·
Invited seminar: Eugene
Wigner Colloquium, TU Berlin, 16 Feb 2017

·
Invited speaker: Workshop
on Cascade Processes: Mathematical Modeling and
Applications, ETH Zurich, 19-20 Jan 2017.

·
Invited speaker: 2016 Summer Solstice
- 8th International Conference on Discrete Models of Complex Systems, Aveiro,
Portugal, 20-22 Jun 2016

**Codes and data: **

·
The simulation codes and network data used in our paper Gleeson JP and
Durrett R, “Temporal profiles of avalanches on networks”, arXiv:1612.06477 can be downloaded from here.

·
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.

**Research areas and selected publications. 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.

Mathematics and Statistics homepage