Patterns of Cascading Behavior in Large Blog Graphs

How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we address in this work. Blogs (weblogs) have become an important medium of information because of their timely publication, ease of use, and wide availability. In fact, they often make headlines, by discussing and discovering evidence about political events and facts. Often blogs link to one another, creating a publicly available record of how information and influence spreads through an underlying social network. Aggregating links from several blog posts creates a directed graph which we analyze to discover the patterns of information propagation in blogspace, and thereby understand the underlying social network. Here we report some surprising findings of the blog linking and information propagation structure, after we analyzed one of the largest available datasets, with 45, 000 blogs and ≈ 2.2 million blog-postings. Our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks.

[1]  Ravi Kumar,et al.  On the Bursty Evolution of Blogspace , 2003, WWW '03.

[2]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[4]  Albert-László Barabási,et al.  Modeling bursts and heavy tails in human dynamics , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, WWW '04.

[6]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[7]  Christos Faloutsos,et al.  Cascading Behavior in Large Blog Graphs , 2007 .

[8]  Jure Leskovec,et al.  Patterns of Influence in a Recommendation Network , 2006, PAKDD.

[9]  Matthew Hurst,et al.  Deriving marketing intelligence from online discussion , 2005, KDD '05.

[10]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[11]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

[12]  Lada A. Adamic,et al.  Tracking information epidemics in blogspace , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

[13]  J. Leskovec,et al.  Cascading Behavior in Large Blog Graphs Patterns and a model , 2006 .

[14]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[15]  Christos Faloutsos,et al.  Data mining meets performance evaluation: fast algorithms for modeling bursty traffic , 2002, Proceedings 18th International Conference on Data Engineering.

[16]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[17]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[18]  Mark S. Granovetter.  Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[19]  Ravi Kumar,et al.  Structure and evolution of online social networks , 2006, KDD '06.

[20]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[21]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.