Complex systems

Complex Systems

Propuesta de tesi Investigadores/as Grupo de investigación

Modelling dynamics on urban complex systems

Urban areas are socio-economic systems that take up space in a particular geography. They are built upon tightly knitted layers of personal interactions and infrastructural networks (communication, transportation, services) offering, in turn, outstanding opportunities for living and working. However, their impact on the surrounding environment is extensive leaving behind a significant footprint – not only ecological, but also in terms of public health, social discrimination, etc. This view of cities creates interdisciplinary opportunities and challenges, bringing together data science, multiplex network science and social science, with consequences for policy decision makers, or landscape and urban planners.

With the increasing popularity of open data technologies and portals, a vast amount of information about these layers is now available to us. This information ranges from structural and dynamical information about the different cities’ infrastructures (collected by sensors deployed in a city) to social information about urban dwellers.

Within this topic, the group’s activities revolve around the following lines:

  • City growth and scaling in cities. The understanding of how cities evolve is crucial in order to predict urban sprawl, urban decay and “conurbation” (population growth and expansion that produce a continuous urban developed area).
  • Dynamics of transportation systems. Data-driven research, exploiting complexity theory (complex networks, percolation, self-organized criticality, agent-based modelling), helps in understanding the internal city dynamics and achieving sustainability from a transportation perspective, improving efficiency and performance [1, 2]. This includes multimodal transportation systems: surface (private and public) connected to underground (public) transport, represented as multiplex complex networks [3].
  • Congestion games. Increasing coordination between the different users that use a limited common good (eg roads) is crucial for the optimal functioning of cities. Lines of research on this topic include designing mechanisms to enhance coordination or modelling the behaviour of users playing these games.
  • City resilience comprises the assessment and understanding of the robustness [4], “stress limits”, and recovery capacity of the networks of infrastructures. These measures are strongly related to crisis response in case of emergencies (traffic re-routing, accident assistance).

[1] A Solé-Ribalta, S Gómez and A Arenas. Royal Society Open Science 3 160098 (2016)

[2] A Solé-Ribalta, S Gómez and A Arenas. Congestion induced by the structure of multiplex networksPhysical Review Letters 116(10), 108701 (2016)

[3] M De Domenico, A Solé-Ribalta, S Gómez and A Arenas. Navigability of interconnected networks under random failuresPNAS 111(23), 8351-8356 (2014)

[4] S Abbar, T Zanouda and J Borge-Holthoefer. Robustness and Resilience of cities around the world5th International Workshop on Urban Computing (UrbComp’16) (2016).

Dr Albert Solé-Ribalta

Dr Javier Borge-Holthoefer

Complex Systems @ IN3-COSIN

Computational Social Science

After four decades of contributions from “sociophysics” and “econophysics”, it was clear, at the turn of the century, that huge challenges –and new opportunities– lied ahead: the digital communication technologies, and their associated data deluge, began to nurture those efforts with empirical significance. Only a decade later, the advent of the Web 2.0, the Internet of Things, and a general adoption of mobile technologies have convinced researchers that theories can be mapped to real scenarios and put into empirical test, closing in this way the experiment-theory cycle in the best scientific tradition.

We are nowadays at a crossroads, at which different approaches converge. We name such crossroads Computational Social Science: a new discipline that can offer powerful models and methods (mainly from Complex Systems), large storage, algorithms and computational power (Computer and Data Science), and a conceptual framework for the results to be interpreted (Social Science).

Within this topic, the group’s activities revolve around the following lines:

  • Information ecosystems: detect, quantify and understand competitive/cooperative dynamics in temporally evolving, information-driven systems. Modeling and analysis of bipartite mutualistic networks: the user-information interplay [1].
  • Information diffusion in online social networks: Social contagion processes at individual and aggregate levels, temporal and geographical patterns of information diffusion, online socio-political mobilizations [2,3].
  • Community detection and dynamic community structure analysis: modeling, tracking and forecasting dynamic groups in social media. Alternative approaches to the analysis of mesoscale structure in unipartite and bipartite networks [3].
  • Social simulation: cultural and opinion dynamics, including the empirical calibration and validation of agent-based social models [4].

See “At the Crossroads: Lessons and Challenges in Computational Social Science” for an introductory text.

[1] J Borge-Holthoefer, RA Baños, C Gracia-Lázaro and Y Moreno. The nested assembly of collective attention in online social systemsScientific Reports (2016)

[2] J Borge-Holthoefer, N Perra, B Gonçalves, S González-Bailón, A Arenas, Y Moreno and A Vespignani. The dynamic of information-driven coordination phenomena: a transfer entropy analysisScience Advances 2(4), e1501158 (2016)

[3] J Borge-Holthoefer and S González-Bailón. Scale, Time, and Activity Patterns: Advanced Methods for the Analysis of Online Networks. Chapter contribution to N Fielding, G Blank and R Lee (eds.) Handbook of Online Research Methods (2nd edition), Thousand Oaks: Sage (2016)

[4] J Borge-Holthoefer, A Rivero, Y Moreno. Locating privileged spreaders on an online social networkPhysical Review E 85, 066123 (2012)

Dr Javier Borge-Holthoefer


Dr Albert Solé-Ribalta

Complex Systems @ IN3-COSIN