Evaluating Disseminators for Time-critical Information Diffusion on Social Networks

In recent years, information diffusion in social networks has received significant attention from the Internet research community driven by many potential applications such as viral marketing and sales promotions. One of the essential problems in information diffusion process is how to select a set of influential nodes as the initial nodes to disseminate the information through their social network. Most of the existing solutions aim at how to maximize the influence effectiveness of the initially selected "influential nodes", but pay little attention on how the influential nodes selection could minimize the cost of the diffusion. Diffusion effectiveness is important for the applications such as innovation and new technology diffusion. However, many applications, such as disseminating disaster information or product promotions, have the mission to deliver messages in a minimal time. In this paper, we design and implement an efficiently k-best social sites selected mechanism in such that the total diffusion “social cost” required for each user in this social network to receive the diffusion critical time information is minimized.

[1]  SaitoKazumi,et al.  Blocking links to minimize contamination spread in a social network , 2009 .

[2]  Michiel H. M. Smid,et al.  A linear-space algorithm for distance preserving graph embedding , 2009, Comput. Geom..

[3]  Yu Wang,et al.  Community-based greedy algorithm for mining top-K influential nodes in mobile social networks , 2010, KDD.

[4]  Masahiro Kimura,et al.  Discovering Influential Nodes for SIS Models in Social Networks , 2009, Discovery Science.

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

[6]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[7]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[8]  Masahiro Kimura,et al.  Blocking links to minimize contamination spread in a social network , 2009, TKDD.

[9]  Huan Liu,et al.  Blogosphere: research issues, tools, and applications , 2008, SKDD.

[10]  D. Watts,et al.  Influentials, Networks, and Public Opinion Formation , 2007 .

[11]  P. Herr,et al.  Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective , 1991 .

[12]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[13]  Nick Koudas,et al.  Efficient identification of starters and followers in social media , 2009, EDBT '09.

[14]  Masahiro Kimura,et al.  Efficient Estimation of Influence Functions for SIS Model on Social Networks , 2009, IJCAI.

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

[16]  Masahiro Kimura,et al.  Extracting influential nodes on a social network for information diffusion , 2009, Data Mining and Knowledge Discovery.

[17]  A. Guttman,et al.  A Dynamic Index Structure for Spatial Searching , 1984, SIGMOD 1984.

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

[19]  Masahiro Kimura,et al.  Extracting Influential Nodes for Information Diffusion on a Social Network , 2007, AAAI.

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

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

[22]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[23]  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.

[24]  Masahiro Kimura,et al.  Finding Influential Nodes in a Social Network from Information Diffusion Data , 2009 .