SUPERCOMPUTER SIMULATION OF CRITICAL PHENOMENA IN COMPLEX SOCIAL SYSTEMS
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For citation: Sloot P.M.A., Holyst J., Kampis G., Lees M.H., Mityagin S.А., Ivanov S.V., Bochenina K.О., Guleva V.Yu., Mukhina K.D., Nasonov D.А., Butakov N.А., Leonenko V.А., Lantseva А.А., Boukhanovsky А.V. Supercomputer simulation of critical phenomena in complex social systems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 6, pp. 967–995. doi: 10.17586/2226-1494-2016-16-6-967-995
The paper describes a problem of computer simulation of critical phenomena in complex social systems on a petascale computing systems in frames of complex networks approach. The three-layer system of nested models of complex networks is proposed including aggregated analytical model to identify critical phenomena, detailed model of individualized network dynamics and model to adjust a topological structure of a complex network. The scalable parallel algorithm covering all layers of complex networks simulation is proposed. Performance of the algorithm is studied on different supercomputing systems. The issues of software and information infrastructure of complex networks simulation are discussed including organization of distributed calculations, crawling the data in social networks and results visualization. The applications of developed methods and technologies are considered including simulation of criminal networks disruption, fast rumors spreading in social networks, evolution of financial networks and epidemics spreading.
Acknowledgements. This paper is financially supported by the Russian Scientific Foundation, Agreement No.14–21–00137 (15.08.2014).
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Fig. 1.Daily schedule of influenza in Saint Petersburg from July 1st, 1987 to June 30,1988. The blue color denotes deviation in data due to the failure to take into account cases by the health authorities in the festive period. Blue and red horizontal lines mark the average levels of the incidence of acute respiratory viral infection (ARVI) in summer, autumn and spring periods, respectively. The red solid line marks the incidence of disease during the epidemic of influenza. The moments of occurrence of critical conditions (transitions from seasonal ARVI to epidemic and back) are marked by arrows
Fig.2. GEV scaling for samples of different sizes obtained from Barabasi– Albert graph with the size equal to 25600 nodes (empirical – empirical distribution, fitted – GEV evaluation based on empirical distribution, scaled – distribution, obtained by squaring fitted for sample of half the size). Averaging over 1000 runs (a); GEV scaling for samples from the graph AutonomousSystems (routers in the Internet), the graph size is 6400 nodes. The size of the set of samples for estimation of GEV is 250 (b)
Fig. 3. A general technique for modeling of critical states in complex social systems based on the formalism of complex networks with the models "A"–"B" (GA genetic algorithm)
Fig. 4. Organization of communication between subnets (a); operation algorithm of the M-process (b); operation algorithm of the L-process (in)
Fig. 5. Parallel efficiency: as-route initiating matrix, Pact=0.5, Prec =0.2, C=2.05, Pini =0,01, N=25, Avg – averaging over different sizes of graphs (a); Erdos–Renyi initiating matrix, Pact=0.4, Prec=0,142, C=2,05, Pini=0.1, N=50 and Barabasi–Albert initiating matrix (for parameters see Fig. 5, a) (b)
Fig. 6 The number of activated nodes and the activation time for variousL-processes for Barabasi–Albert initiating matrix Pact=0.3, Prec=0.2, C=2.005, Pini=0.015, N=35, V=230(a); efficiency for different number of M processes, as route initiating matrix, V=230(b)
Albert initiating matrix (for parameters see Fig. 5, a) (b)
Fig. 7. The number of edges that are added in an iteration for various types of initiating matrices Pact=0.5, Prec=0.3, C=2.1, Pini=0.015, N=35, Nk=24 (a); the number of activated vertices for different Pact, initiating Barabasi–Albert matrix with parameters: Nk=25, Prec=0.1, N=25 (b)
Fig. 8. A general diagram of the architecture of the modeling complex of critical phenomena of complex social systems
Fig. 9. Performance evaluation of data collection depending on the number of crawler agents on the example of the Twitter social network: the time (in seconds) to gather information on 4,000 users (a); the number of collected users for a fixed time – 7 min (b)
a) b) c)
Fig. 10. An example of dynamic process visualization of rumors spread via mobile communication networks: distribution of awareness at the initial time moment (a); after 20 hours of model time (b); after the end of modeling (b)
Fig. 11. Examples of visualization algorithm of complex networks: graph of treatment profiles of patients in a large medical center (a); a chain of posts from the social network VKontakte (b)
Fig. 12. The creation of epidemiological contact network based on the MA-modeling of people’s mobility at the micro level: a fragment of the movement scene of agents in an urban environment (a); modeling of infection propagation processes in contact network (b)
Fig. 13. Effectiveness evaluation of optimization methods of information dissemination process in Twitter: by the solutions found by GA (genetic algorithm is a genetic algorithm, green line) and solutions with the greatest number of subscribers HD (highest degree is the highest degree of the vertex, red line): the relevance of the tweet for 24 hours (a); the relevance of the tweet 12 hours (b)
Fig. 14. Model dynamics of changes in the banking network to the complete destruction as a result of a cascade of contagious defaults (а–g). The size of the dots corresponds to the values of the bank assets, the color corresponds to the estimated number of iterations before the bank default. The graphs in the figures (from top to bottom) reflect the dynamics of changes in the number of bank defaults in the system, temperature, entropy, and the proportion of nodes with negative dynamics
Fig. 15. The restoration of criminal network fragment followed by the separation of three combinations of chains
Fig. 16. The outcomes of two destruction strategies: removal of vertices with the highest degree (a); removal of vertices with the most frequent appearance in various combinations of chains (b)
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