ABSTRACT
In the blog network, the posts in a blog can be diffused to other blogs through trackbacks and scraps. Analyzing information diffusion in the blog network is an important research issue that can be used for predicting information diffusion, detecting abnormality, marketing, and revitalizing the blog world. Existing studies on information diffusion in a blog network define explicit relationships between blogs and analyze the word-of-mouth effect through such explicit relationships only. However, it has been observed that more than 85% of all information diffusion in a blog network occurs through non-explicit relationships. In this paper, we propose a new model that considers both the explicit and non-explicit relationships between blogs in order to explain the information diffusion phenomena in a blog network. We add a super node and the relationships between the super node and blogs as broadcast edges and register edges to the existing information diffusion model and assign the assimilation probability to every relationship. The expanded information diffusion model improves the accuracy of the basic model by taking into account the degrees of diffusion powers of posts. We verify the superiority of the proposed model through extensive experiments of information diffusion at a real blog network. The experimental results show that our expanded information diffusion model generates 77% less errors than the existing model.
- B. Aaron et al., "Equating R-Based and D-Based Effect-Size Indices: Problems with a Commomly Recommended Formula," Florida Educational Research Association, 1998.Google Scholar
- L. Adamic, O. Buyukkokten, and E. Adar, "A Social Network Caught in the Web," First Monday, Vol. 8, No. 6, pp. 1--22, 2003.Google Scholar
- N. Agarwal et al., "Identifying the influential bloggers in a community," In Proc. Int'l. Conf. on Web Search and Web Data Mining, WSDM, pp. 207--218, 2008. Google ScholarDigital Library
- A. Java et al., Modeling the Spread of Influence on the Blogosphere, Technical Report TR-CS-06-03, University of Maryland, Baltimore, 2006.Google Scholar
- C. Asavathiratham et al., "The Influence Model," In Proc. IEEE Int'l. Conf. on Control Systems, pp. 52--64, 2001.Google Scholar
- R. Albert, H. Jeong, and A. Barabasi, "Diameter of the World Wide Web," Nature, Vol. 47, pp. 651--654, 2000.Google Scholar
- Blogger.com Co., Ltd. http://blogger.comGoogle Scholar
- J. Brown and P. Reinegen, "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Vol. 1, No. 3, pp. 350--362, 1987.Google ScholarCross Ref
- C. Ratanamahatana, and E. Keogh, "Making Time-Series Classification More Accurate Using Learned Constraints," In Proc. SIAM Int'l. Conf. on Data Mining SDM 2004.Google Scholar
- SK Communications, http://www.cyworld.comGoogle Scholar
- D. Gruhl et al., "Information Diffusion Through Blogspace," In Proc. Int'l. Conf. on World Wide Web, WWW, pp. 491--501, 2004. Google ScholarDigital Library
- P. Domingos and M. Richardson, "Mining the Network Value of Customers," In Proc. ACM Int'l. Conf. on Knowledge Discovery and Data Mining, ACM SIGKDD, pp. 57--66, 2001. Google ScholarDigital Library
- G. Ellison, "Learning, Local Interaction, and Coordination," Econometrica, Vol. 61, No. 5, pp. 1047--1071, 1993.Google ScholarCross Ref
- Empas Corp., http://www.empas.comGoogle Scholar
- F. Duarte et al., "Traffic Characteristics and Communication Patterns in Blogosphere," In Proc. Int'l. Conf. on Weblogs and Social Media, ICWSAM, 2007.Google Scholar
- J. Goldenberg, B. Libai, and E. Muller, "Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth," Marketing Letters, Vol. 12, No. 3, pp. 211--223, 2001.Google ScholarCross Ref
- M. Granovetter, "The Strength of Weak Ties," American Journal of Sociology, Vol. 78, No. 6, pp. 1360--1380, 1973.Google ScholarCross Ref
- M. Granovetter, "Threshold Models of Collective Behavior," American Journal of Sociology, Vol. 86, No. 6, pp. 1420--1443, 1978.Google ScholarCross Ref
- iSAVEZONE Corp., http://www.isavezone.comGoogle Scholar
- E. Keogh, "Exact Indexing of Dynamic Time Warping," In Proc. Int'l. Conf. on Very Large Data Bases, VLDB, pp. 406--417, 2002. Google ScholarDigital Library
- D. Kempe, J. Kleinberg, and E. Tardos, "Maximizing the Spread of Influence through a Social Network," In Proc. ACM Int'l. Conf. on Knowledge Discovery and Data Mining, ACM SIGKDD, pp. 137--146, 2003. Google ScholarDigital Library
- S. Kim, S. Park, and W. Chu, "An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases," In Proc. IEEE Int'l. Conf. on Data Engineering, IEEE ICDE, pp. 607--614, 2001. Google ScholarDigital Library
- R. Kumar, J. Novak, and A. Tomkins, "Structure and Evolution of Online Social Networks," In Proc. Int'l. Conf. on Knowledge Discovery and Data, pp. 611--617, 2006. Google ScholarDigital Library
- M. McGlohon et al., "Finding Patterns in Blog Shapes and Blog Evolution," In Proc. Int'l. Conf. on Weblogs and Social Media, 2007.Google Scholar
- S. Milgram, "The Small World Problem," Physiology Today, Vol. 2, pp. 60--67, 1967.Google Scholar
- MySpace.com Co., Ltd. http://www.myspace.comGoogle Scholar
- NHN Corp., http://www.naver.comGoogle Scholar
- A. Nowak, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press. 2000.Google Scholar
- S. Redner, "How Popoular Is Your Paper?," European Physics Journal B, Vol. 4, No. 2, pp. 131--134, 1998.Google ScholarCross Ref
- S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, 1994.Google ScholarCross Ref
- D. Watt and S. Strogatz, "Collective Dynamics of 'Small-World'Networks," Nature, Vol. 393, pp. 440--442, 1998.Google ScholarCross Ref
- D. Watts, Small Worlds: The Dynamics of Networks Between Order and Randomness, Princeton, New Jersey: Princeton University Press, 1999. Google ScholarDigital Library
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The information diffusion model in the blog world
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