A novel information cascade model in online social networks

The spread and diffusion of information has become one of the hot issues in today’s social network analysis. To analyze the spread of online social network information and the attribute of cascade, in this paper, we discuss the spread of two kinds of users’ decisions for city-wide activities, namely the “want to take part in the activity” and “be interested in the activity”, based on the users’ attention in “DouBan” and the data of the city-wide activities. We analyze the characteristics of the activity-decision’s spread in these aspects: the scale and scope of the cascade subgraph, the structure characteristic of the cascade subgraph, the topological attribute of spread tree, and the occurrence frequency of cascade subgraph. On this basis, we propose a new information spread model. Based on the classical independent diffusion model, we introduce three mechanisms, equal probability, similarity of nodes, and popularity of nodes, which can generate and affect the spread of information. Besides, by conducting the experiments in six different kinds of network data set, we compare the effects of three mechanisms above mentioned, totally six specific factors, on the spread of information, and put forward that the node’s popularity plays an important role in the information spread.

[1]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

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

[3]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[4]  H. Stanley,et al.  Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation , 2012, Scientific Reports.

[5]  Charles A. Holt,et al.  Classroom Games: Information Cascades , 1996 .

[6]  Bernhard Schölkopf,et al.  Modeling Information Propagation with Survival Theory , 2013, ICML.

[7]  Rediet Abebe.  Can Cascades be Predicted? , 2014 .

[8]  Masato Hisakado,et al.  Information Cascade, Kirman’s Ant Colony Model, and Kinetic Ising Model , 2014, 1401.4258.

[9]  Charles A. Holt,et al.  Information Cascades in the Laboratory , 1998 .

[10]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

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

[12]  Christos Faloutsos,et al.  Modeling Blog Dynamics , 2009, ICWSM.

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

[14]  Harry Eugene Stanley,et al.  Cascade of failures in coupled network systems with multiple support-dependent relations , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  S. Milgram,et al.  Note on the drawing power of crowds of different size. , 1969 .

[16]  Bernhard Schölkopf,et al.  Uncovering the structure and temporal dynamics of information propagation , 2014, Network Science.

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

[18]  E. Rogers.  Diffusion of Innovations , 1962 .

[19]  I. Welch.  Sequential Sales, Learning, and Cascades , 1992 .

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

[21]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[22]  Jon M. Kleinberg,et al.  Sequential Influence Models in Social Networks , 2010, ICWSM.