Abstract
Models for the processes by which ideas and influences propagate through a social network have been studied in number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of ’word of mouth’ in the promotion of new products. The problem of selecting a set of most influential nodes in a network has been proved to be NP-hard. We propose a framework to analyze the network in depth and to find the set of most influential nodes. We consider the problem of selecting, for any given positive integer k, the most influential k nodes in a Academic Social Network (ASN), based on certain criterions relevant in academic environment like number of citations, working location of authors, cross reference and cross co-authorship. Based on the initial node set selection and the diffusion model, we study the spread of influence of the influential nodes in the academic network. Appropriate criterions are used in the proposed generalized diffusion models. In this paper, we used two different models; (1) Linear Threshold Model and (2) Independent Cascade model to find the set of influential nodes for different criterions in an ASN and compared their performances. We constructed ASN based on the information collected from DBLP, Citeseer and used Java Social Network Simulator (JSNS) for experimental simulations.
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References
Suri, N., Narahari, Y.: Determining the top-k nodes in social networks using the Shapley value. In: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems. International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, vol. 3, pp. 1509–1512 (2008)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137–146. ACM, New York (2003)
Kempe, D., Kleinberg, J., Tardos, É.: Influential nodes in a diffusion model for social networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005)
Newman, M.: The structure and function of complex networks. Arxiv preprint cond-mat/0303516 (2003)
Jensen, D., Neville, J.: Data mining in social networks. In: Dynamic Social Network Modeling and Analysis: workshop summary and papers, pp. 7–9 (2002)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 57–66. ACM, New York (2001)
McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and role discovery in social networks. In: Proceedings of IJCAI, Citeseer, vol. 19, pp. 786–791 (2005)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the twelfth international conference on Information and knowledge management, pp. 556–559. ACM, New York (2003)
Fernandess, Y., Malkhi, D.: On spreading recommendations via social gossip. In: Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures, pp. 91–97. ACM, New York (2008)
Kossinets, G., Kleinberg, J.M., Watts, D.J.: The structure of information pathways in a social communication network. In: KDD, 435–443 (2008)
Portes, A.: Social capital: its origins and applications in modern sociology. Annual Review of Sociology 24(1), 1–24 (1998)
Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology 83(6), 14–20 (1978)
Valente, T.: Network models of the diffusion of innovations. Computational & Mathematical Organization Theory 2(2), 163–164 (1996)
Young, H.: The diffusion of innovations in social networks. In: Blume, L.E., Durlauf, S.N. (eds.) The Economy As an Evolving Complex System III: Current Perspectives and Future Directions. Oxford University Press, USA (2003)
Young, H.: Individual strategy and social structure: An evolutionary theory of institutions. Princeton University Press, Princeton (1998)
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Junapudi, V., Udgata, G.K., Udgata, S.K. (2010). Study of Diffusion Models in an Academic Social Network. In: Janowski, T., Mohanty, H. (eds) Distributed Computing and Internet Technology. ICDCIT 2010. Lecture Notes in Computer Science, vol 5966. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11659-9_29
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DOI: https://doi.org/10.1007/978-3-642-11659-9_29
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