In this study, we present a new network model to address the question of how social networks such as groups in social websites, networks of social events, online video game groups evolve in time. A common feature of all these networks is that first, new users attach to an existing group (clique), and second, the decision of new network users is greatly influenced by already-joined users. In our model, new user joins the underlying network from a parallel network layer as the consequence of influence of underlying network users on the ones from other networks. This influence could be any type of relationship exterior to the underlying network layer such as friendship. For the new network model design, we utilize the concepts behind: 1. Growing network model presented by Barabási–Albert (BA), 2. Social influence-driven contagion model and 3. Multiplex network model. The way we treat this challenging phenomena is by multilayer projection, i.e. friends of each actual user that exist in other social network layers are projected as virtual users onto the underlying network layer. At each time step, each virtual user might become actual user based on a proposed Social influence-driven contagion method.

 

Published In : IJCSN Journal Volume 2, Issue 6

Date of Publication : 01 December 2013

Pages : 114 - 119

Figures : 02

Tables : 01

Publication Link : ijcsn.org/IJCSN-2013/2-6/IJCSN-2013-2-6-147.pdf

 

 

 

Kambiz Behfar : Department of International Business, HEC Montreal, H3T 2A7, Canada.

Ekaterina Turkina : Department of International Business, HEC Montreal, H3T 2A7, Canada.

Patrick Cohendet : Department of International Business, HEC Montreal, H3T 2A7, Canada.

Animesh Animesh : Department of Information Systems, Faculty of Management, McGill University, H3A 1G5, Canada.

 

 

 

 

 

 

 

Social network

growth

contagion

Multilayer

 

 

 

We presented a new network model to address the question of how social networks such as groups in social websites, network of social events, online video game groups grow in time. In this network model, a new user attaches to a network layer from another network layer as the consequence of influence of underlying network users on the ones from other networks. We treated this challenging phenomena by multilayer projection, i.e. friends of each user belonging to other networks were projected as virtual users onto the underlying network layer containing the groups of actual users.

 

 

 

 

 

 

 

 

 

[1] Erdos, P. andRényi,A. (1960) "On the evolution of random graphs" Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5: 17–61

[2] Watts, D. J.; Strogatz, S. H. (1998) "Collective dynamics of 'small-world' networks" Nature 393 (6684).

[3] Barabasi, A.L. and Albert,R. (1999) “Emergence of scaling in random networks” Science Vol 286.

[4] Barabasi, A.L., Alberta, R., and Jeong,H. (2008) “Mean-field theory for scale-free random networks” Physica A, 272.

[5] Bianconi, G. andBarabasi, A. L.(2001) “Competition and multiscaling in evolving networks” EurophysLett. 54, 436.

[6] Servedio,V. D. P. and Caldarelli,G.(2004) “Vertex Intrinsic Fitness: How to Produce Arbitrary Scale-Free Networks” Phys Rev E 70, 056126.

[7] Caldarelli,G.,Capocci, A., Rios,P. D. L., and Munoz,M. A.(2002) “Scale-free networks from varying vertex intrinsic fitness.” Phys. Rev. Lett. 89, 258702.

[8] Liben-Nowell,D.,Kleinberg,J. (2008) “Tracing Information Flow on a Global Scale Using Internet Chain-Letter Data. Proc. National Academy of Sciences, 105(12):4633–4638.

[9] Domingos, P. and Richardson, M. (2001) "Mining the Network Value of Customers," Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining (pp. 57-66), San Francisco, CA: ACM Press.

[10] Hartmann, W.R., (2010) "Demand Estimation with Social Interactions and the Implications for Targeted Marketing," Marketing Science, 29(4).

[11] Chen W, Wang Y, Yang S.(2009)“Efficient influence maximization in social networks” Proc. 15th ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining.

[12] Fang,X. (2013) “Predicting Adoption Probabilities in Social Networks” Information systems Research, Vol. 24.

[13] Aral, S., Muchnik, L. and Sundararajan, A. (2013) “Engineering Social Contagions: Optimal Network Seeding in the Presence of Homophily” Network Science.

[14] Aral,A. and Walker, D. (2011) “Creating Social contagion through viral product design: A randomized trial of peer influence in networks” Management Science, 57(9), Sep 1623-1639.

[15] Aral,A. and Walker, D. (2012) “Identifying influential and susceptible members of social networks” Science, 337 (6092): 337-341.

[16] Gomex, S. et al (2013) “Diffusion dynamics on multiplex networks” Phys Rev. Lett. 110, 028701.

[17] Gomez-Gardenes, J. et al (2012) “Evolution of cooperation in multiplex networks”.

[18] Podobnik, B. et al (2012) “Preferential attachment in the interaction between dynamically generated interdependent networks” A Letters Journal exploring the frontiers of physics (EPL) , 100, 50004.

[19] Son, S.W.,Bizhani,G., Christensen, C., Grassberger, P. , and Paczuski, M. (2012) “Percolation theory on interdependent networks based on epidemic spreading” A letter journal exploiting the frontiers of physics.

[20] Wajam:http://www.wajam.com/.

[21] SuperRewards: http://www.superrewards.com/.