Social networks: modeling structure and dynamics

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Doctoral thesis (article-based)
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Department of Biomedical Engineering and Computational Science publications. A, Report, 11
The study of networks of social interaction can be seen to originate from the work of Jacob Moreno in the 1920's. At the turn of the millennium new actors entered the field, researchers with a background in physics and computer science, who brought with them a new set of tools that could be used to collect and analyse large sets of data. Analysis of large scale social network data from various sources has increased our knowledge of the common features of various social networks, observed in networks of acquaintance and collaboration alike. The quantification and modeling of a particular feature of social networks, namely the tendency of individuals to form densely connected groups with relatively few links to individuals outside the group (called communities in complex networks theory), has taken large steps in recent years. Modeling these structures and their effect on social dynamics is a highly topical issue, relevant for fields such as spreading of epidemics or rumors and formation of opinions, with applications such as prevention of epidemics and marketing. This thesis aims to increase our understanding of the structure of large scale social networks, and of dynamics unfolding in such networks, in several ways: 1) In order to answer a need for social network models that generate realistic structures at large scale, we introduce a model based on simple local mechanisms leading to community structure. 2) A thorough comparative study of models for social networks assesses the adaptability of the models to fit real social network data, and their success at reproducing prominent structural features of social networks. In discussing in detail two major approaches to modeling social networks, this study may promote the understanding between researchers from the two 'schools of thought'. 3) We study models of competing options, with focus on perhaps the most important feature of social network structure, namely communities, that had been largely lacking in earlier research.
social networks, network models, community structure, social dynamics, models of competing options
  • [Publication 1]: R. Toivonen, J.-P. Onnela, J. Saramäki, J. Hyvönen, and K. Kaski: A Model for Social Networks. Physica A, 371(2), 851 (2006).
  • [Publication 2]: R. Toivonen, L. Kovanen, M. Kivelä, J.-P. Onnela, J. Saramäki, and K. Kaski: A comparative study of social network models: network evolution models and nodal attribute models. Helsinki University of Technology, Technical report A10, ISBN 978-951-22-9764-1 (2009).
  • [Publication 3]: R. Toivonen, J. Kumpula, J. Saramäki, J.-P. Onnela, J. Kertész, and K. Kaski: The role of edge weights in social networks: modelling structure and dynamics. In Noise and Stochastics in Complex Systems And Finance, edited by J. Kertész, S. Bornholdt, and R.N. Mantegna. In Proceedings of SPIE, Vol. 6601, 66010B-1 (2007).
  • [Publication 4]: X. Castelló, R. Toivonen, V. M. Eguíluz, J. Saramäki, K. Kaski, and M. San Miguel: Anomalous lifetime distributions and topological traps in ordering dynamics. Europhysics Letters, 79, 66006 (2007).
  • [Publication 5]: X. Castelló, R. Toivonen, V. M. Eguíluz, M. San Miguel: Modelling bilingualism in language competition: the effects of complex social structure. In Proceedings of the 4th Conference of the European Social Simulation Association (ESSA 07). IRIT Editions (2007).
  • [Publication 6]: X. Castelló, R. Toivonen, V. M. Eguíluz, L. Loureiro-Porto, J. Saramäki, K. Kaski, M. San Miguel: Modelling language competition: bilingualism and complex social networks. In The evolution of language; Proceedings of the 7th International Conference (EVOLANG7). Edited by A.D.M. Smith, K. Smith, and R. Ferrer-Cancho, World Scientific Publishing Co. (2008).
  • [Publication 7]: R. Toivonen, X. Castelló, J. Saramäki, V. M. Eguíluz, K. Kaski, and M. San Miguel: Broad lifetime distributions for ordering dynamics in complex networks. Physical Review E, 79, 016109 (2009).