Social Network Analysis and Mining for Business Applications

Authors : Francesco Bonchi , Carlos Castillo , Aristides Gionis , Alejandro Jaimes Authors Info & Claims

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Abstract

Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored.

In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.

References

Abrol, M., Mahadevan, U., McCracken, K., Mukherjee, R., and Raghavan, P. 2002. Social networks for enterprise webs. In Proceedings of the International World Wide Web Conference (WWW'02).

Adar, E. and Adamic, L. A. 2005. Tracking information epidemics in blogspace. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

Agarwal, G. and Kempe, D. 2008. Modularity-Maximizing network communities via mathematical programming. Euro. Phys. J. B 66, 3.

Agarwal, N., Liu, H., Tang, L., and Yu, P. S. 2008. Identifying the influential bloggers in a community. In Proceedings of the 1^stInternational Conference on Web Search and Web Data Mining, (WSDM'08).

Aggarwal, C. C. and Yu, P. S. 2005. Online analysis of community evolution in data streams. In Proceedings of the SIAM International Data Mining Conference (SDM'05).

Aggarwal, G., Mishra, N., and Pinkas, B. 2004. Secure computation of the kth-ranked element. In Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT).

Agrawal, R. and Srikant, R. 2000a. Privacy-Preserving data mining. In Proceedings of the ACM-SIGMOD International Conference on Management of Data (SIGMOD). 439--450.

Agrawal, R. and Srikant, R. 2000b. Privacy-Preserving data mining. In Proceedings of the ACM SIGMOD International Conference on Management of Data.

Ahn, H.-K., Cheng, S.-W., Cheong, O., Golin, M. J., and van Oostrum, R. 2001. Competitive facility location along a highway. In Proceedings of the 7th Annual International Conference on Computing and Combinatorics, (COCOON'01).

Ahuja, R. K., Magnanti, Thomas L., and Orlin, J. B. 1993. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, Englewood Cliffs, NJ.

Amatriain, X., Jaimes, A., Oliver, N., and Pujol, J. M. 2010. Data Mining Methods for Recommender Systems. Springer, Chapter 2, 39--100.

Anagnostopoulos, A., Kumar, R., and Mahdian, M. 2008. Influence and correlation in social networks. In Proceedings of the 14^thACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08).

Aral, S. 2010. Identifying social influence: A comment on opinion leadership and social contagion in new product diffusion. SSRN eLibrary.

Aral, S., Brynjolfsson, E., and Van Alstyne, M. W. 2006. Information, technology and information worker productivity. SSRN eLibrary.

Aral, S., Brynjolfsson, E., and Van Alstyne, M. W. 2007. Productivity effects of information diffusion in networks. SSRN eLibrary.

Aral, S., Muchnik, L., and Sundararajan, A. 2009. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Nat. Acad. Sci. 106, 51, 21544--21549.

Aral, S. and Van Alstyne, M. W. 2010. Networks, information and brokerage: The diversity-bandwidth tradeoff. SSRN eLibrary.

Aral, S. and Walker, D. 2010. Creating social contagion through viral product design: A Randomized trial of peer influence in networks. SSRN eLibrary.

Arthur, D., Motwani, R., Sharma, A., and Xu, Y. 2009. Pricing strategies for viral marketing on social networks. In Proceedings of the 5^thInternational Workshop on Internet and Network Economics (WINE'09). 101--112.

Arthur, W. B. 1989. Competing technologies, increasing returns, and lock-in by historical events. Econ. J. 99, 394.

Backstrom, L., Dwork, C., and Kleinberg, J. M. 2007. Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography. In Proceedings of the International World Wide Web Conference (WWW'07). 181--190.

Backstrom, L., Huttenlocher, D. P., Kleinberg, J. M., and Lan, X. 2006. Group formation in large social networks: Membership, growth, and evolution. In Proceedings of the 12^thACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06).

Bakshy, E., Karrer, B., and Adamic, L. A. 2009. Social influence and the diffusion of user-created content. In Proceedings 10^thACM Conference on Electronic Commerce (EC'09).

Balog, K., Azzopardi, L., and De Rijke, M. 2006. Formal models for expert finding in enterprise corpora. In Proceedings of the 29^thAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, 43--50.

Balog, K. and De Rijke, M. 2007. Determining expert profiles (with an application to expert finding). In Proceedings of the 20^thInternational Joint Conference on Artifical Intelligence. Morgan Kaufmann, San Francisco, CA, 2657--2662.

Barabasi, A. L. and Albert, R. 1999. Emergence of scaling in random networks. Sci. 286, 5439, 509--512.