Modeling and simulation of e-mail social networks: A new stochastic agent-based approach

Understanding how the structure of a network evolves over time is one of the most interesting and complex topics in the field of social networks. In our attempt to model the dynamics of such systems, we explore an agent-based approach to model growth of email-based social networks, in which individuals establish, maintain and allow atrophy of links through contact-lists and e-mails. The model is based on the idea of common neighbors, but also on a detailed specialization of the classical preferential attachment theory, thus capturing a deeper understanding of the topology of inter-node connections. In our event-based simulation that schedules the agents¿ actions over time, the proposed model is amenable to significant efficiency improvements through an application of the Gillespie stochastic simulation schemes. Computer simulation results are used to validate the model by showing that its unique features endow it with ability to simulate real-world email networks with convincing realism.

[1]  H. Kowarzyk.  Structure and Function. , 1910, Nature.

[2]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[3]  Shlomo Hershkop,et al.  Automated social hierarchy detection through email network analysis , 2007, WebKDD/SNA-KDD '07.

[4]  Camille Roth.  Measuring Generalized Preferential Attachment in Dynamic Social Networks , 2005, ArXiv.

[5]  Guido Caldarelli,et al.  Preferential attachment in the growth of social networks: the case of Wikipedia , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[7]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[8]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

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

[10]  D. Gillespie.  Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

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

[12]  D. Watts,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2001 .

[13]  Michael J. North,et al.  Experiences creating three implementations of the repast agent modeling toolkit , 2006, TOMC.

[14]  Bud Mishra,et al.  Complexities, Catastrophes and Cities: Emergency Dynamics in Varying Scenarios and Urban Topologies , 2010 .

[15]  M Girvan,et al.  Structure of growing social networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  E. Levanon,et al.  Preferential attachment in the protein network evolution. , 2003, Physical review letters.

[17]  Jafar Adibi,et al.  The Enron Email Dataset Database Schema and Brief Statistical Report , 2004 .

[18]  Terrill L. Frantz,et al.  Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different” , 2005, Comput. Math. Organ. Theory.

[19]  Jie Wu,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2003 .