Участник:StasFomin/SocialInfluencers

• Discovering leaders from community actions (Goyal, Bonchi, Lakshmanan, 2008)
• Group formation in large social networks. membership, growth, and evolution (Backstrom, Huttenlocher, Kleinberg, Lan, 2006)
• The mathematical theory of infectious diseases and its applications (Bailey, 1975) — недоступно и не в тему.
• Everyones an influencer. quantifying influence on twitter (Bakshy, Hofman, Mason, Watts, 2011)
• TwitterRank. Finding Topic-sensitive Influential Twitterers (Weng, Lim, Jiang, He, 2010)

• [4] E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts.

Everyone’s an influencer: quantifying influence on twitter. In WSDM, pages 65–74, 2011.

• [5] F. M. Bass. A new product growth for model consumer

durables. Manage. Sci., 50:1825–1832, December 2004.

• [6] J. Baumes, M. Goldberg, and M. Magdon-Ismail. Efficient

identification of overlapping communities. In In IEEE International Conference on Intelligence and Security Informatics (ISI), pages 27–36, 2005.

• [7] F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida.

Detecting spammers on twitter. In Proceedings of the 7th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS), 2010.

• [8] S. Bharathi, D. Kempe, and M. Salek. Competitive influence

maximization in social networks. In WINE, pages 306–311, 2007.

• [9] J. Bollen, H. Mao, and X.-J. Zeng. Twitter mood predicts the

stock market. CoRR, abs/1010.3003, 2010.

• [10] J. Bollen, A. Pepe, and H. Mao. Modeling public mood and

emotion: Twitter sentiment and socio-economic phenomena. CoRR, abs/0911.1583, 2009.

• [11] C. Budak, D. Agrawal, and A. El Abbadi. Where the blogs

tip: connectors, mavens, salesmen and translators of the blogosphere. In SIGKDD Workshop on Social Media Analytics, 2010.

• [12] C. Budak, D. Agrawal, and A. El Abbadi. Limiting the

spread of misinformation in social networks. In WWW, 2011.

• [13] C. Budak, D. Agrawal, and A. El Abbadi. Structural trend

analysis for online social networks. Technical Report UCSB/CS-2011-04, UCSB, 2011.

• [14] T. Carnes, C. Nagarajan, S. M. Wild, and A. van Zuylen.

Maximizing influence in a competitive social network: a follower’s perspective. In ICEC ’07, pages 351–360, New York, NY, USA, 2007. ACM.

• [15] M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi.

Measuring user influence in twitter: The million follower fallacy. In ICWSM, 2010.

• [16] M. Cha, A. Mislove, B. Adams, and K. P. Gummadi.

Characterizing social cascades in flickr. In Proceedings of the first workshop on Online social networks, WOSP ’08, pages 13–18, New York, NY, USA, 2008. ACM.

• [17] W. Chen, C. Wang, and Y. Wang. Scalable influence

maximization for prevalent viral marketing in large-scale social networks. In KDD, pages 1029–1038, 2010.

• [18] W. Chen, Y.Wang, and S. Yang. Efficient influence

maximization in social networks. In Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, pages 199–208, 2009.

• [19] A. Clauset. Finding local community structure in networks.

Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 72(2):026132, 2005.

• [20] O. Diekmann and J. Heesterbeek. Mathematical

epidemiology of infectious diseases: model building, analysis, and interpretation. Wiley, 2000.

• [21] P. S. Dodds and D. J. Watts. A generalized model of social

and biological contagion. Journal of Theoretical Biology, 232:587–604, 2005.

• [22] P. Domingos and M. Richardson. Mining the network value

of customers. In Proceedings of the 7th ACM International Conference on Knowledge Discovery and Data Mining, pages 57–66, 2001.

• [23] P. Dubey, R. Garg, and B. D. Meyer. Competing for

customers in a social network: The quasi-linear case. In WINE, pages 162–173, 2006.

• [24] C. for Computational Analysis of Social and O. S. (CASOS).

Casos networks. http://www.casos.cs.cmu.edu/ computational_tools/data2.php.

• [25] S. Garriss, M. Kaminsky, M. J. Freedman, B. Karp,

D. Mazires, and H. Yu. Re: Reliable email. In In Proc. NSDI, pages 297–310, 2006.

• [26] R. Ghosh and K. Lerman. Community detection using a

measure of global influence. In Proceedings of the 2nd KDD Workshop on Social Network Analysis (SNAKDD’08), 2008.

• [27] M. Gladwell. The Tipping Point: How Little Things Can

Make a Big Difference. Back Bay Books, January 2002.

• [28] M. Gomez-Rodriguez, J. Leskovec, and A. Krause. Inferring

networks of diffusion and influence. In KDD, pages 1019–1028, 2010.

• [29] S. Gregory. An algorithm to find overlapping community

structure in networks. Knowledge Discovery in Databases: PKDD 2007, pages 91–102, 2007.

• [30] C. Grier, K. Thomas, V. Paxson, and M. Zhang. @spam: the

underground on 140 characters or less. In Proceedings of the 17th ACM conference on Computer and communications security, CCS ’10, pages 27–37, New York, NY, USA, 2010. ACM.

• [31] L. Grossman. Iran protests: Twitter, the medium of the

movement. Time (online), June 2009.

• [32] R. Guimera and L. Amaral. Functional cartography of

complex metabolic networks. Nature, 433(7028):895–900, 2005.

• [33] K. M. Heussner. Enough already! 7 twitter hoaxes and

half-truths. ABC News, January 2010.

• [34] J. Horrigan and L. Rainie. When facing a tough decision, 60

million americans now seek the internet’s help: The internet’s growing role in life’s major moments [retrieved october 13, 2006]. http://pewresearch.org/obdeck/?ObDeckID=19, 2006.

• [35] A. L. Hughes and L. Palen. Twitter adoption and use in mass

4convergence and emergency events. In Proceedings of the 6th International Information Systems for Crisis Response and Management Conference, 2009.

• [36] Tweetstats. http://tweetstats.com/trends.
• [37] Trendistic. http://trendistic.com/.
• [38] T. N. Jagatic, N. A. Johnson, M. Jakobsson, and F. Menczer.

Social phishing. Commun. ACM, 50:94–100, October 2007.

• [39] D. Kempe, J. M. Kleinberg, and ´E. Tardos. Maximizing the

spread of influence through a social network. In Proceedings of the Ninth ACM International Conference on Knowledge Discovery and Data Mining, pages 137–146, 2003.

• [40] M. Kimura, K. Saito, R. Nakano, and H. Motoda. Social

Computing and Behavioral Modeling, chapter Finding Influential Nodes in a Social Network from Information Diffusion Data. Springer US, 2009.

• [41] J. Kostka, Y. A. Oswald, and R. Wattenhofer. Word of

mouth: Rumor dissemination in social networks. In SIROCCO, pages 185–196, 2008.

• [42] R. Kumar, J. Novak, and A. Tomkins. Structure and evolution

of online social networks. In KDD, pages 611–617, 2006.

• [43] H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a

social network or a news media? http://an.kaist.ac.kr/traces/WWW2010.html.

• [44] H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a

social network or a news media? In WWW ’10, pages 591–600, 2010.

• [45] C.-A. La and P. Michiardi. Characterizing user mobility in

second life. In Proceedings of the first workshop on Online social networks, WOSP ’08, pages 79–84, New York, NY, USA, 2008. ACM.

• [46] J. Leskovec. Stanford large network dataset collection.

http://snap.stanford.edu/data/index.html, 2009.

• [47] J. Leskovec, L. A. Adamic, and B. A. Huberman. The

dynamics of viral marketing. TWEB, 1(1), 2007.

• [48] J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking

and the dynamics of the news cycle. In KDD ’09, pages 497–506, 2009.

• [49] J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. M.

VanBriesen, and N. S. Glance. Cost-effective outbreak detection in networks. In KDD, pages 420–429, 2007.

• [50] S. Liu, L. Ying, and S. Shakkottai. Influence maximization in

social networks: An ising-model-based approach. In Allerton, 2010.

• [51] A. Mislove, H. S. Koppula, K. P. Gummadi, P. Druschel, and

B. Bhattacharjee. Growth of the flickr social network. In Proceedings of the first workshop on Online social networks, WOSP ’08, pages 25–30, New York, NY, USA, 2008. ACM.

• [52] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and

B. Bhattacharjee. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, IMC ’07, pages 29–42, New York, NY, USA, 2007. ACM.

• [53] E. Morozov. Swine flu: Twitter’s power to misinform.

Foreign Policy, April 2009.

• [54] M. Newman. Network data.

http://www-personal.umich.edu/˜mejn/netdata/.

• [55] M. E. J. Newman. Fast algorithm for detecting community

structure in networks. Physical Review E, 69, September 2003.

• [56] M. E. J. Newman. Modularity and community structure in

networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, June 2006.

• [57] A. Pothen, H. D. Simon, and K.-P. Liou. Partitioning sparse

matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl., 11(3):430–452, 1990.

• [58] M. Richardson and P. Domingos. Mining knowledge-sharing

sites for viral marketing. In Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining, pages 61–70, 2002.

• [59] T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes

twitter users: real-time event detection by social sensors. In WWW, pages 851–860, 2010.

• [60] J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D.

Lieberman, and J. Sperling. Twitterstand: news in tweets. In GIS ’09, pages 42–51, 2009.

• [61] T. Schelling. Micromotives and macrobehavior. WW Norton

& Company, 2006.

• [62] J. P. Scott. Social Network Analysis: A Handbook. SAGE

Publications, January 2000.

• [63] C. Smith. Egypt’s facebook revolution: Wael ghonim thanks

the social network. The Huffington Post, February 2011.

• [64] G. Swamynathan, C. Wilson, B. Boe, K. Almeroth, and B. Y.

Zhao. Do social networks improve e-commerce?: a study on social marketplaces. In Proceedings of the first workshop on Online social networks, WOSP ’08, pages 1–6, New York, NY, USA, 2008. ACM.

• [65] B. E. Teitler, M. D. Lieberman, D. Panozzo,

J. Sankaranarayanan, H. Samet, and J. Sperling. Newsstand: a new view on news. In GIS ’08, pages 1–10, 2008.

• [66] Twitter. http://www.twitter.com.
• [67] A. H. Wang. Don’t follow me - spam detection in twitter. In

SECRYPT, pages 142–151, 2010.

• [68] Y. Wang, G. Cong, G. Song, and K. Xie. Community-based

greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of the 16th ACM International Conference on Knowledge Discovery and Data Mining, 2010.

• [69] J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding

topic-sensitive influential twitterers. In WSDM, pages 261–270, 2010.

• [70] Twitter: We are not keeping wikileaks out of trending topics.

http://mashable.com/2010/12/06/ wikileaks-twitter-censorship/.

• [71] C. Wilson, B. Boe, A. Sala, K. P. Puttaswamy, and B. Y.

Zhao. User interactions in social networks and their implications. In EuroSys, pages 205–218, 2009.

• [72] S. Yardi, D. M. Romero, G. Schoenebeck, and D. Boyd.

Detecting spam in a twitter network. First Monday, 15(1), 2010.

• [73] Y. yeol Ahn, S. Han, H. Kwak, Y. ho Eom, S. Moon, and

H. Jeong. Analysis of topological characteristics of huge online social networking services. In WWW07, pages 835–844. ACM, 2007.

• [74] H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman.

Sybilguard: defending against sybil attacks via social networks. SIGCOMM Comput. Commun. Rev., 36:267–278, August 2006.

• [75] S. Zhang, R. Wang, and X. Zhang. Identification of

overlapping community structure in complex networks using fuzzy c-means clustering. Physica A: Statistical Mechanics and its Applications, 374(1):483–490, 2007.