The Research of Network Cluster Behavior Mode in Online Public Opinion

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Abstract:

In this study, by analysis of the value of public opinion, the node of the 30 the "PM2.5 Chinese name" event, the network cluster of public opinion, the event found examples to study its propagation characteristics. online public opinion perspective, build the adjacency matrix of a network cluster analysis; using four types of centrality and path calculation and analysis of network clusters presented key milestones, and critical path method. Calculated from the social point of view, the corresponding public opinion through the study guide and the entry point of the network cluster control, laid the foundation to further explore the online public opinion research in network cluster behavior patterns.

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244-250

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] P. B. Stephen, M. Ajay, B. J. Daniel, L. Giuseppe (2009). Network Analysis in the Social Sciences. Science, 323: 892-895.

Google Scholar

[2] D. Liben-Nowell and J. Kleinberg (2003). The Link Prediction Problem for Social Networks. 12th International Conference on Information and Knowledge Management, (2003).

DOI: 10.1145/956863.956972

Google Scholar

[3] V. Latora and M. Marchiori (2004). How the science of complex networks can help developing strategies against terrorism. Chaos, Solitons, and Fractals. 20: 69-75.

DOI: 10.1016/s0960-0779(03)00429-6

Google Scholar

[4] M Girvan and M. E. J. Newman (2001). Community structure in social and biological networks. Proceeding of National Academy of Sciences, 99: 7821-7826.

Google Scholar

[5] M. Batty (1971). Modelling Cities as Dynamic Systems. Nature, 231: 425-428.

Google Scholar

[6] D. Liben-Nowell and J. Kleinberg (2003). The Link Prediction Problem for Social Networks. 12th International Conference on Information and Knowledge Management, (2003).

DOI: 10.1145/956863.956972

Google Scholar

[7] A. L. Barabasi (2005). The origin of bursts and heavy tails in humans dynamics. Nature, 435, 207-211.

Google Scholar

[8] Zhang, Liu. The evolution of the virtual community network [J]. Physics, 2008 (9).

Google Scholar

[9] X. Li, X. F. Wang and G. Chen (2004). Pinning a complex dynamical network onto its equilibrium. IEEE Transactions on Circuits and Systems-I, 51(10): 2074-(2087).

DOI: 10.1109/tcsi.2004.835655

Google Scholar

[10] CROSS R, PRUSAK L. The people who m ak e organ izations go orstop [ J]. H arvard Bus iness Rev iew, 2002, 7: 104 - 112.

Google Scholar