Analyzing characteristics of information propagation on social network graphs

Social networking sites, such as YouTube, Flickr, Livejournal and Okrut, are becoming very popular with a large number of Internet users. As a consequence, information propagation on social network sites is becoming more important and has been studied by many researchers. However, most of the researches have been focused on statistical analysis to understand the static characteristics of social network graphs. In this paper, we will employ simulation based empirical study to better understand information diffusion process on social networking sites. According to our limited experiments, online social network graphs are more dense than the traditional social networks. It requires only three time steps to cover 90% nodes of the networks with two or three initial nodes.

[1]  Balaji Rajagopalan,et al.  Knowledge-sharing and influence in online social networks via viral marketing , 2003, CACM.

[2]  Jure Leskovec,et al.  Planetary-scale views on a large instant-messaging network , 2008, WWW.

[3]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

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

[5]  Devavrat Shah,et al.  Gossip Algorithms , 2009, Found. Trends Netw..

[6]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[7]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[8]  Pedro M. Domingos.  Mining Social Networks for Viral Marketing , 2022 .

[9]  M Kretzschmar,et al.  Measures of concurrency in networks and the spread of infectious disease. , 1996, Mathematical biosciences.

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

[11]  Marco Rosa,et al.  Four degrees of separation , 2011, WebSci '12.

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

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

[14]  Fernando Vega-Redondo,et al.  Complex Social Networks: Searching in Social Networks , 2007 .

[15]  Steffen Staab,et al.  Social Networks Applied , 2005, IEEE Intell. Syst..

[16]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[17]  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.

[18]  D. Watts,et al.  An Experimental Study of Search in Global Social Networks , 2003, Science.