Network science and computational social science allow us to understand collective human behaviour by analysing data and making models of the digital interactions we have online. The closing gap between off- and online activities lets us perform this task better than ever, bringing both knowledge of the large-scale structure of society, and challenges in predicting the future behaviour of individuals. Here are some of my research lines in this amazing field

Highlight: Generic features of rank dynamics in complex systems

Many complex systems develop rankings of their elements that emerge from networked interactions. These rankings evolve according to system-dependent mechanisms of interaction and reflect the relevance of elements in performing a function in the system. By analysing ranking data on many social, biological, and economic systems, as well as simple models of rank dynamics, we show there are generic features of rank stability that allow us to model and predict patterns of ranking behaviour across complex systems

  • G. Iñiguez, C. Pineda, C. Gershenson, A.-L. Barabási. Dynamics of ranking. Nature Communications 13, 1646 (2022). DOI: arXiv: 2104.13439
  • M. Janosov, F. Musciotto, F. Battiston, G. Iñiguez. Elites, communities and the limited benefits of mentorship in electronic music. Scientific Reports 10, 3136 (2020). DOI: 10.1038/s41598-020-60055-w. arXiv: 1908.10968
  • J. E. Snellman, G. Iñiguez, J. Kertész, R. A. Barrio, K. Kaski. Status maximization as a source of fairness in a networked dictator game. Journal of Complex Networks cny022 (2018). DOI: 10.1093/comnet/cny022. arXiv: 1806.05542
  • J. A. Morales, E. Colman, S. Sánchez, F. Sánchez-Puig, C. Pineda, G. Iñiguez, G. Cocho, J. Flores, C. Gershenson. Rank dynamics of word usage at multiple scales. Frontiers in Physics 6, 45 (2018). DOI: 10.3389/fphy.2018.00045. arXiv: 1802.07258
  • S. Sánchez, G. Cocho, J. Flores, C. Gershenson, G. Iñiguez, C. Pineda. Trajectory stability in the traveling salesman problem. Complexity 2018, 2826082 (2018). DOI: 10.1155/2018/2826082. arXiv: 1708.06945
  • J. E. Snellman, G. Iñiguez, T. Govezensky, R. A. Barrio, K. Kaski. Modelling community formation driven by the status of individual in a society. Journal of Complex Networks 5, 6, 817–838 (2017). DOI: 10.1093/comnet/cnx009. arXiv: 1702.02541
  • J. A. Morales, S. Sánchez, J. Flores, C. Pineda, C. Gershenson, G. Cocho, J. Zizumbo, R. F. Rodríguez, G. Iñiguez. Generic temporal features of performance rankings in sports and games. EPJ Data Science 5, 33 (2016). DOI: 10.1140/epjds/s13688-016-0096-y. arXiv: 1606.04153

Highlight: Complex contagion on techno-social networks

We use large-scale data from online social platforms to study and predict the temporal evolution of social contagion processes and cascading behaviour related to the use of innovations and new digital markets. By considering data features like weighted or multiplex social interactions, product competition,
and temporal social networks, we aim at increasing the explanatory and predictability power of social contagion models

  • S. Unicomb, G. Iñiguez, J. P. Gleeson, M. Karsai, Dynamics of cascades on burstiness-controlled temporal networks. Nature Communications 12, 133 (2020). DOI: arXiv: 2007.06223
  • S. Unicomb, G. Iñiguez, J. Kertész, M. Karsai. Reentrant phase transitions in threshold driven contagion on multiplex networks. Physical Review E 100, 040301(R) (2019). DOI: 10.1103/PhysRevE.100.040301. arXiv: 1901.08306
  • G. Iñiguez, Z. Ruan, K. Kaski, J. Kertész, M. Karsai. Service adoption spreading in online social networks. In S. Lehmann and Y.-Y. Ahn, eds., Spreading Dynamics in Social Systems (Springer Nature, 2018). DOI: 10.1007/978-3-319-77332-2. arXiv: 1706.09777
  • S. Unicomb, G. Iñiguez, M. Karsai. Threshold driven contagion on weighted networks. Scientific Reports 8, 3094 (2018). DOI: 10.1038/s41598-018-21261-9. arXiv: 1707.02185
  • M. Karsai, G. Iñiguez, R. Kikas, K. Kaski, J. Kertész. Local cascades induced global contagion: How heterogeneous thresholds, exogenous effects, and unconcerned behaviour govern online adoption spreading. Scientific Reports 6, 27178 (2016). DOI: 10.1038/srep27178. arXiv: 1601.07995
  • Z. Ruan, G. Iñiguez, M. Karsai, J. Kertész. Kinetics of social contagion. Physical Review Letters 115, 218702 (2015). DOI: 10.1103/PhysRevLett.115.218702. arXiv: 1506.00251
  • M. Karsai, G. Iñiguez, K. Kaski, J. Kertész. Complex contagion process in spreading of online innovation. Journal of the Royal Society Interface 11, 20140694 (2014). DOI: 10.1098/rsif.2014.0694arXiv: 1405.6879

Highlight: Cumulative effects of triadic closure and homophily in social networks

With a mean-field bifurcation analysis of a simple model of social network evolution, we estimate how much of the observed homophily in friendship and communication networks is an amplification due to triadic closure, which explains the rise of segregated groups in society. Homophily-based models also allow us to infer attitudinal space embeddings for social network data.

  • P. Ramaciotti, J.-P. Cointet, G. Muñoz, A. F. Peralta, G. Iñiguez, A. Pournaki, Inferring attitudinal spaces in social networks. Social Network Analysis and Mining 13, 14 (2022). DOI:
  • A. Asikainen, G. Iñiguez, J. Ureña-Carrión, K. Kaski, M. Kivelä. Cumulative effects of triadic closure and homophily in social networks. Science Advances 6, eaax7310 (2020). DOI: 10.1126/sciadv.aax7310. arXiv: 1809.06057

Conflict emergence and resolution in Wikipedia

We validate opinion-formation models with data to emulate the
temporal evolution of conflicts between editors in Wikipedia. We find three typical scenarios for the way editors disagree while writing articles: short conflicts, plateaus of consensus among fights, and uninterrupted controversy. We wish not only to identify the ways by which people manage to collaborate despite their differences, but to propose efficient measures of prevention and control to minimise the harmful effects of social conflict

  • G. Iñiguez, J. Török, T. Yasseri, K. Kaski, J. Kertész. Modeling social dynamics in a collaborative environment. EPJ Data Science 3, 7 (2014). DOI: 10.1140/epjds/s13688-014-0007-z. arXiv: 1403.3568
  • G. Iñiguez, Statistical Physics of Opinion and Social Conflict (Aalto University publication series, Helsinki, 2013). ISBN: 978-952-60-5108-6
  • J. Török, G. Iñiguez, T. Yasseri, M. San Miguel, K. Kaski, J. Kertész. Opinions, conflicts, and consensus: Modeling social dynamics in a collaborative environment. Physical Review Letters 110, 088701 (2013). DOI: 10.1103/PhysRevLett.110.088701. arXiv: 1207.4914

Opinion formation, algorithmic bias, and deception on coevolving social networks

Opinions synthesize our perceptions and the knowledge we have of the external world, other people, and ourselves. To better understand the impact opinions have on social interactions and decision-making, we perform small controlled experiments and analyse idealized models of opinion formation, deception, and network change, potentially under the effect of algorithmic bias, letting us gauge how opinions influence the structure and dynamics of off- and online society

  • R. A. Barrio, T. Govezensky, R. Dunbar, G. Iñiguez, K. Kaski. Dynamics of deceptive interactions in social networks. Journal of the Royal Society Interface 12, 20150798 (2015). DOI: 10.1098/rsif.2015.0798. arXiv: 1509.03918
  • G. Iñiguez, T. Govezensky, R. Dunbar, K. Kaski, R. A. Barrio. Effects of deception in social networks. Proceedings of the Royal Society B 281, 20141195 (2014). DOI: 10.1098/rspb.2014.1195. arXiv: 1406.0673
  • G. Iñiguez, J. Tagüeña-Martínez, K. Kaski, R. A. Barrio. Are opinions based on science: Modelling social response to scientific facts. PLoS ONE 7, e42122 (2012). DOI: 10.1371/journal.pone.0042122. arXiv: 1109.1488
  • G. Iñiguez, J. Kertész, K. Kaski, R. A. Barrio. Phase change in an opinion-dynamics model with separation of time scales. Physical Review E 83, 016111 (2011). DOI: 10.1103/PhysRevE.83.016111. arXiv: 1009.2643
  • G. Iñiguez, R. A. Barrio, J. Kertész, K. Kaski. Modelling opinion formation driven communities in social networks. Computer Physics Communications 182, 1866–1869 (2011). DOI: 10.1016/j.cpc.2010.11.020. arXiv: 1007.4177
  • G. Iñiguez, J. Kertész, K. Kaski, R. A. Barrio. Opinion and community formation in coevolving networks. Physical Review E 80, 066119 (2009). DOI: 10.1103/PhysRevE.80.066119. arXiv: 0908.1068v2

Model-driven selection of optimal urban and socioeconomic policies

By combining large-scale data on multilayer urban mobility and socioeconomic networks with toy models and optimization algorithms, we devise data-modelling frameworks to explore the effect of external policies on urban and socioeconomic development. These platforms will suggest optimal policies given societal and economic constraints to, say, maximize use of bikes while minimizing changes to the mobility network

  • L. G. Natera Orozco, F. Battiston, G. Iñiguez, M. Szell. Data-driven strategies for optimal bicycle network growth. Royal Society open science 7, 201130 (2020). DOI: 10.1098/rsos.201130. arXiv: 1907.07080
  • L. G. Natera Orozco, F. Battiston, G. Iñiguez, M. Szell, Extracting the multimodal fingerprint of urban transportation networks. Transport Findings, June (2020). DOI: 10.32866/001c.13171. arXiv: 2006.03435
  • G. Castañeda, G. Iñiguez, F. Chávez-Juárez, The complex network of public policies. An empirical framework for identifying their relevance in economic development. World Development Report 2017, Governance and the Law (World Bank background paper, 2017). WEB:

Upcoming: Games as controlled experiments of social behavior

In collaboration with gaming companies, we want to co-develop games designed to test hypotheses of social activity related to cooperative versus competitive behavior, homophilous interactions, and social group formation. With statistical inference methods and machine-learning algorithms, we want to identify the most relevant features of individuals and game design that predict engagement. This analysis will help us validate long-standing theories of social behavior, as well as improve business models of partner gaming companies

Other works

  • M. Zumaya, R. Guerrero, E. Islas, O. Pineda, C. Gershenson, G. Iñiguez, C. Pineda, Identifying tax evasion in Mexico with tools from network science and machine learning. In O. M. Granados and J. R. Nicolás-Carlock, eds., Corruption Networks: Concepts and Applications (Springer Cham, 2021). DOI: arXiv: 2104.13353
  • G. Iñiguez, H.-H. Jo, K. Kaski, Special Issue “Computational Social Science”. Information 2019, 10, 307 (2019). DOI: 10.3390/info10100307
  • J. Tagüeña-Martínez, G. Iñiguez, K. Kaski, R. A. Barrio, Modeling social networks response to scientific information. 12th International Public Communication of Science and Technology Conference, PCST 2012 (Observa Science in Society, Florence, 2012). ISBN: 978-88-904514-9-2
  • G. Iñiguez. Física estadística de los sistemas sociales. Revista Digital Universitaria 11, 54 (2010). ISSN: 1607-6079
  • G. Iñiguez, Dinámica de Redes y Coevolución en Sistemas Complejos (Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, 2009)
  • G. Iñiguez, R. A. Barrio. Coevolution in social networks. Educación Química 20, 272–279 (2009). ISSN: 0187-893X

contact me

gerardo.iniguez [AT] tuni [DOT] fi

Tampere University, Finland

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