The Genetic Algorithm as a General Diffusion Model for Social Networks

Diffusion processes taking place in social networks are used to model a number of phenomena, such as the spread of human or computer viruses, and the adoption of products in 'viral marketing' campaigns. It is generally difficult to obtain accurate information about how such spreads actually occur, so a variety of stochastic diffusion models are used to simulate spreading processes in networks instead. We show that a canonical genetic algorithm with a spatially distributed population, when paired with specific forms of Holland's synthetic hyperplane-defined objective functions, can simulate a large and rich class of diffusion models for social networks. These include standard diffusion models, such as the independent cascade and competing processes models. In addition, our genetic algorithm diffusion model (GADM) can also model complex phenomena such as information diffusion. We demonstrate an application of the GADM to modeling information flow in a large, dynamic social network derived from e-mail headers.

[1]  H. Hethcote.  Three Basic Epidemiological Models , 1989 .

[2]  M. Keeling,et al.  The effects of local spatial structure on epidemiological invasions , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[3]  R. Sepulchre,et al.  Collective optimization over average quantities , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

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

[5]  Thomas W. Valente.  Network models of the diffusion of innovations , 1996, Comput. Math. Organ. Theory.

[6]  Christos Faloutsos,et al.  Epidemic thresholds in real networks , 2008, TSEC.

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

[8]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[9]  Bo Peng,et al.  Empirical Analysis of the Spatial Genetic Algorithm on Small-World Networks , 2006, International Conference on Computational Science.

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Camille Roth,et al.  How Realistic Should Knowledge Diffusion Models Be? , 2007, J. Artif. Soc. Soc. Simul..

[12]  John H. Holland,et al.  Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions , 2000, Evolutionary Computation.

[13]  Shishir Bharathi,et al.  Competitive Influence Maximization in Social Networks , 2007, WINE.

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

[15]  M. Newman.  Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Rajmonda Sulo Caceres,et al.  The Impact of Structural Changes on Predictions of Diffusion in Networks , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[17]  T. Valente,et al.  Network models of the diffusion of innovations , 1995, Comput. Math. Organ. Theory.

[18]  J. Kleinberg.  Algorithmic Game Theory: Cascading Behavior in Networks: Algorithmic and Economic Issues , 2007 .

[19]  James Aspnes,et al.  Worm Versus Alert: Who Wins in a Battle for Control of a Large-Scale Network? , 2007, OPODIS.

[20]  Tanya Y. Berger-Wolf,et al.  Finding Spread Blockers in Dynamic Networks , 2008, SNAKDD.

[21]  Chaitanya Swamy,et al.  Approximation Algorithms for the Firefighter Problem: Cuts over Time and Submodularity , 2009, ISAAC.

[22]  Christian Becker,et al.  An epidemic model for information diffusion in MANETs , 2002, MSWiM '02.

[23]  Margaret J. Eppstein,et al.  Emergent mating topologies in spatially structured genetic algorithms , 2006, GECCO.