Analysis of the distribution of electoral fields using network structures
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2016.3.06Keywords:
Cellular automata, complex networks, electoral field, modularity, modeling public opinionAbstract
Approaches based on mathematical modeling to analyze the distribution of electoral fields were studied. Cellular automata and complex network models were analyzed. Cellular automata explain the formation of stable electoral regions, but are poorly suited for the real prediction. The advantage was given to the networks due to the possibility of taking into account the uneven spread of the public opinion. The connection between network communities and formation of stable electoral regions was considered. It is proposed to use the modularity to find the community boundaries which can explain existed electoral fields and predict the distribution of new electoral regions. To verify the selected approach, the distribution of electoral fields of the south of Ukraine was analyzed using data from parliamentary elections in 2014.References
Ralko O.N. Vozniknovenie i razvitie elektoral'noj geografii v SShA i Zapadnoj Evrope / O.N. Ralko // Uchenye zapiski Tavricheskogo natsional'nogo universiteta im. V.I. Vernadskogo. Serija "Geografija". — 2012. — 25 (64). — № 2. — S. 147–152.
Brown T.A. Nonlinear Politics / T.A. Brown // Chaos Theory in the Social Sciences. — 1996. — P. 119–137.
Terpil I.O. Simulation of Public Opinion with Ideas of Cellular Automata / I.O. Terpil A.S. Makarenko // Lecture Notes in Computer Science. — 8751. — P. 518–525.
Granovetter M.S. The Strength of Weak Ties / M.S. Granovetter // American Journal of Sociology. — 1973. — 78, Issue 6. — P. 1360–1380.
Horbulin V.P. Informatsijni operatsiyi ta bezpeka suspil'stva: zahrozy, protydija, modeljuvannja: monohr. / V.P. Horbulin, O.H. Dodonov, D.V. Lande. — K.: Intertekhnolohija, 2009. — S. 153–162.
Barabasi A.-L. Statistical mechanics of complex networks / A.-L. Barabasi, R. Albert // Reviews of Modern Physics 74. — 2012. —74. — P. 47–97.
Newman M.E.J. Modularity and community structure in networks / M.E.J. Newman // Proceeding of the National Academy of Sciences of the United States of America. — 2006. —103. — P. 8577–8582.
Blondel V. D. Fast unfolding of communities in large networks / V.D. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre // Journal of Statistical Mechanics: Theory and Experiment. — 2008. — 10. — P. 1000.
Lambiotte R. Laplacian Dynamics and Multiscale Modular Structure in Networks / R. Lambiotte, J.-C. Delvenne, M. Barahona // IEEE Transaction on Network Science and Engineering. — 2015. —1, Issue 2. — P. 76–90.
Chen M. Community Detection via Maximization of Modularity and Its Variants / M. Chen, K. Kuzmin, B.K. Szymanski // IEEE Transactions on Computational Social Systems. — 2014. —1(1). — P. 46–65.
Azizifard N. Social Network Clustering / N. Azizifard // I.J. Information Technology and Computer Science. — 2014. —01. — P. 76–81.
Adamic L.A. The political blogosphere and the 2004 u.s. election: divided they blog / L.A. Adamic, N. Glance // Proceedings of the 3rd international workshop on Link discovery. — New York: LinkKDD. — 2005. — P. 36–43.
Guerra P.H.C. A Measure of Polarization on Social Media Networks Based on Community Boundaries / P.H.C. Guerra, W.M. Jr, C. Cardie, R. Kleinberg // Available at: https://www.cs.cornell.edu/home/cardie/papers/ICWSM13-Polarization.pdf
Nematzadeh A., Ferrara E., Flammini A., Ahn YY. Optimal network modularity for information diffusion / A. Nematzadeh, E. Ferrara, A. Flammini, YY. Ahn // Phys. Rev. Lett. — 2014. — 113. — P. 8.
