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The identifying of the most influential nodes in the complex network is of great significance for information dissemination and control. We collect actual data from Sina Weibo and establish two user relationship networks based on bi-directional concern. By analyzing the statistical characteristics of the network topology, we find that each of them has a small world and scale free characteristics. Moreover, we describe four network centrality indicators, including node degree, closeness, betweenness and k-Core. Through empirical analysis of four-centrality metric distribution, we find that the node degrees follow a segmented power-law distribution; betweenness difference is most significant; both networks possess significant hierarchy, but not all of the nodes with higher degree have the greater k-Core values; strong correlation exists between the centrality indicators of all nodes, but this correlation is weakened in the node with higher degree value. The two networks are used to simulate the information spreading process with the SIR information dissemination model based on infectious disease dynamics. The simulation results show that there are different effects on the scope and speed of information dissemination under different initial selected individuals. We find that the closeness and k-Core can be more accurate representations of the core of the network location than other indicators, which helps us to identify influential nodes in the information dissemination network.
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Keywords:
- microblog /
- centrality /
- complex network /
- information transmission /
- k-Core
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[7] Mislove A, Marcon M, Gummadi K P, Druschel P, Bhattacharjee B 2007 Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement San Diego, USA, October 24-26, 2007 p29
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[22] Borge-Holthoefer J, Moreno Y 2012 Phys. Rev. E 85 026116
[23] Chen D B, Lü L Y, Shang M S, Zhang Y C, Zhou T 2012 Physica A 391 1777
[24] Zhou X, Zhang F M, Li K W, Hui X B, Wu H S 2012 Acta Phys. Sin. 61 50201 (in Chinese) [周漩, 张凤鸣, 李克武, 惠晓滨, 吴虎胜 2012 物理学报 61 50201]
[25] Holme P, Kim B J, Yoon C N, Han S K 2002 Phys. Rev. E 65 056109
[26] Zhang Y C, Liu Y, Zhang H F, Cheng H, Xiong F 2011 Acta Phys. Sin. 60 50501 (in Chinese) [张彦超, 刘云, 张海峰, 程辉, 熊菲 2011 物理学报 60 50501]
[27] Xiong X, Hu Y 2012 Acta Phys. Sin. 61 150509 (in Chinese) [熊熙, 胡勇 2012 物理学报 61 150509]
[28] Alvarez-Hamelin J I, Dallásta L, Barrat A, Vespignani A 2006 Advances in Neural Information Processing Systems 18 (Cambridge: MIT Press) p41
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[1] Watts D J, Strogatz S H 1998 Nature 393 440
[2] Barabási A L, Albert R 1999 Science 286 509
[3] Newman M E J 2003 SIAM Rev. 45 167
[4] Newman M E J, Park J 2003 Phys. Rev. E 68 036122
[5] Kumar R, Novak J, Tomkins A 2006 Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Philadelphia, USA, August 20-23, 2006 p611
[6] Ahn Y Y, Han S, Kwak H, Moon S, Jeong H 2007 Proceedings of the 16th International Conference on World Wide Web Banff, Canada, May 8-12, 2007 p835
[7] Mislove A, Marcon M, Gummadi K P, Druschel P, Bhattacharjee B 2007 Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement San Diego, USA, October 24-26, 2007 p29
[8] Fu F, Liu L, Wang L 2008 Physica A 387 675
[9] Hu H B, Wang X F 2009 Phys. Lett. A 373 1105
[10] Si X M, Liu Y 2011 Acta Phys. Sin. 60 78903 (in Chinese) [司夏萌, 刘云 2011 物理学报 60 78903]
[11] Java A, Song X, Finin T, Tseng B 2007 Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Snalysis San Jose, USA, August 12, 2007 p56
[12] Kwak H, Lee C, Park H, Moon S 2010 Proceedings of the 19th International Conference on World Wide web Raleigh, USA, April 26-30, 2010 p591
[13] Wasserman S, Faust K 1994 Social Network Analysis: Methods and Applications (New York: Cambridge Univ. Press) p169
[14] Koschôtzki D, Schreiber F 2004 Proceedings of the German Conference on Bioinformatics Bielefeld, Germany, October 4-6, 2004 p199
[15] Guimerá R, Mossa S, Turtschi A, Amaral L A N 2005 Proc. Natl. Acad. Sci. USA 102 7794
[16] Zheng X, Chen J P, Shao J L, Bie L D 2012 Acta Phys. Sin. 61 190510 (in Chinese) [郑啸, 陈建平, 邵佳丽, 别立东 2012 物理学报 61 190510]
[17] Carmi S, Havlin S, Kirkpatrick S, Shavitt Y, Shir E 2007 Proc. Natl. Acad. Sci. USA 104 11150
[18] Cai K Q, Zhang J, Du W B, Cao X B 2012 Chin. Phys. B 21 28903
[19] Paolo C, Vito L, Sergio P 2006 Phys. Rev. E 73 036125
[20] Wang L, Zhang Q Q 2006 Complex Systems and Complexity Science 3 13 (in Chinese) [王林, 张倩倩 2006 复杂系统与复杂性科学 3 13]
[21] Kitsak M, Gallos L K, Havlin S, Liljeros F, Muchnik L, Stanley H E, Makse H A 2010 Nat. Phys. 6 888
[22] Borge-Holthoefer J, Moreno Y 2012 Phys. Rev. E 85 026116
[23] Chen D B, Lü L Y, Shang M S, Zhang Y C, Zhou T 2012 Physica A 391 1777
[24] Zhou X, Zhang F M, Li K W, Hui X B, Wu H S 2012 Acta Phys. Sin. 61 50201 (in Chinese) [周漩, 张凤鸣, 李克武, 惠晓滨, 吴虎胜 2012 物理学报 61 50201]
[25] Holme P, Kim B J, Yoon C N, Han S K 2002 Phys. Rev. E 65 056109
[26] Zhang Y C, Liu Y, Zhang H F, Cheng H, Xiong F 2011 Acta Phys. Sin. 60 50501 (in Chinese) [张彦超, 刘云, 张海峰, 程辉, 熊菲 2011 物理学报 60 50501]
[27] Xiong X, Hu Y 2012 Acta Phys. Sin. 61 150509 (in Chinese) [熊熙, 胡勇 2012 物理学报 61 150509]
[28] Alvarez-Hamelin J I, Dallásta L, Barrat A, Vespignani A 2006 Advances in Neural Information Processing Systems 18 (Cambridge: MIT Press) p41
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