Modelling topic propagation over the Internet

Because of the booming of the Internet, content security is becoming more intractable, because of the emergence of complex contents and the diversity in human activity on the Internet. The article proposes a model for the dynamics of topic propagation over the Internet. Topics on the Internet are considered as clusters of contents on websites, which describe various kinds of events. The model accounts for the behaviours of websites, such as anti-infection ability, recovery ability, spreading ability and effective propagation rate. A new topic diffusion mechanism incorporating Markov model based on topic activity transition is employed in the model. By means of simulations, we explore the time-dependent spreading of topics in directed scale-free networks, in which nodes are considered as websites and directed links represent the source dependencies between websites. The simulation results accord with the actual observation very well.

[1]  Christos Faloutsos,et al.  Patterns of Cascading Behavior in Large Blog Graphs , 2007, SDM.

[2]  Jianping Zeng,et al.  Predictive Model for Internet Public Opinion , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[3]  Tim Oates,et al.  Modeling the Spread of Influence on the Blogosphere , 2006 .

[4]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

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

[6]  A. Barabasi,et al.  Percolation in directed scale-free networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[8]  Katarzyna Sznajd-Weron,et al.  Opinion evolution in closed community , 2000, cond-mat/0101130.

[9]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[10]  Stephanie Forrest,et al.  Email networks and the spread of computer viruses. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  G. Weimann.  www.terror.net – How Modern Terrorism Uses the Internet , 2004 .

[12]  David M. Pennock,et al.  Winners don't take all: Characterizing the competition for links on the web , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  J. Basso.  How public relations professionals are managing the potential for sabotage, rumors, and misinformation disseminated via the Internet by computer hackers , 1997 .

[14]  Dirk Helbing,et al.  Modelling the Dynamics of Disaster Spreading in Networks , 2006 .

[15]  Kouichi Sakurai,et al.  Combating cyber terrorism: countering cyber terrorist advantages of surprise and anonymity , 2003, 17th International Conference on Advanced Information Networking and Applications, 2003. AINA 2003..

[16]  Alessandro Vespignani,et al.  Epidemic dynamics and endemic states in complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  M. L. Martins,et al.  Electoral surveys’ influence on the voting processes: a cellular automata model , 2002 .

[18]  Dietrich Stauffer,et al.  Sociophysics: the Sznajd model and its applications , 2002 .