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Part of the book series: Studies in Big Data ((SBD,volume 1))

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Abstract

The staggering growth of online social networking platforms has also propelled information sharing among users in the network. This has helped develop the user-to-content link structure in addition to the already present user-to-user link structure. These two data structures has provided us with a wealth of dataset that can be exploited to develop a social search engine and significantly improve our search for relevant information. Every user in a social networking platform has their own unique view of the network. Given this, the aim of a social search engine is to analyze the relationship shared between friends of an individual user and the information shared to compute the most socially relevant result set for a search query.

In this work, we present InfoSearch: a social search engine.We focus on how we can retrieve and rank information shared by the direct friend of a user in a social search engine. We ask the question, within the boundary of only one hop in a social network topology, how can we rank the results shared by friends. We develop InfoSearch over the Facebook platform to leverage information shared by users in Facebook. We provide a comprehensive study of factors that may have a potential impact on social search engine results. We identify six different ranking factors and invite users to carry out search queries through InfoSearch. The ranking factors are: ‘diversity’, ‘degree’, ‘betweenness centrality’, ‘closeness centrality’, ‘clustering coefficient’ and ‘time’. In addition to the InfoSearch interface, we also conduct user studies to analyze the impact of ranking factors on the social value of result sets.

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References

  1. Adamic, L., Adar, E.: How to search a social network. Social Networks 27(3), 187–203 (2005), doi:10.1016/j.socnet.2005.01.007

    Article  Google Scholar 

  2. Banerjee, A., Basu, S.: A social query model for decentralized search. In: Proceedings of the 2nd Workshop on Social Network Mining and Analysiss, vol. 124. ACM, New York (2008)

    Google Scholar 

  3. Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks. In: International AAAI Conference on Weblogs and Social Media (2009)

    Google Scholar 

  4. Baumes, J., Goldberg, M., Krishnamoorthy, M., Magdon-Ismail, M., Preston, N.: Finding communities by clustering a graph into overlapping subgraphs. In: International Conference on Applied Computing (2005)

    Google Scholar 

  5. Baumes, J., Goldberg, M., Magdon-Ismail, M.: Efficient identification of overlapping communities. In: Kantor, P., Muresan, G., Roberts, F., Zeng, D.D., Wang, F.-Y., Chen, H., Merkle, R.C. (eds.) ISI 2005. LNCS, vol. 3495, pp. 27–36. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Bhattacharyya, P., Rowe, J., Wu, S.F., Haigh, K., Lavesson, N., Johnson, H.: Your best might not be good enough: Ranking in collaborative social search engines. In: Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing (2011)

    Google Scholar 

  7. Borodin, A., Roberts, G.O., Rosenthal, J.S., Tsaparas, P.: Link analysis ranking: algorithms, theory, and experiments. ACM Transactions on Internet Technology 5(1), 231–297 (2005), doi:10.1145/1052934.1052942

    Article  Google Scholar 

  8. Brandes, U.: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 163–177 (2001)

    Article  MATH  Google Scholar 

  9. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)

    Article  Google Scholar 

  10. Cross, R., Parker, A., Borgatti, S.: A bird’s-eye view: Using social network analysis to improve knowledge creation and sharing. IBM Institute for Business Value (2002)

    Google Scholar 

  11. Davitz, J., Yu, J., Basu, S., Gutelius, D., Harris, A.: iLink: search and routing in social networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 931–940. ACM (2007)

    Google Scholar 

  12. Derényi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Physical Review Letters 94(16), 160, 202 (2005)

    Google Scholar 

  13. Dhyani, D., Ng, W.K., Bhowmick, S.S.: A survey of Web metrics. ACM Computing Surveys 34(4), 469–503 (2002), doi:10.1145/592642.592645

    Article  Google Scholar 

  14. Dodds, P.S., Muhamad, R., Watts, D.J.: An Experimental Study of Search in Global Social Networks. Science 301, 827–829 (2003)

    Article  Google Scholar 

  15. Facebook: Introducing facebook graph search (2013), https://www.facebook.com/about/graphsearch

  16. Fortunato, S.: Community detection in graphs. arXiv 906 (2009)

    Google Scholar 

  17. Girvan, M., Newman, M.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  18. Gregory, S.: A fast algorithm to find overlapping communities in networks. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 408–423. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  19. Gregory, S.: Finding Overlapping Communities Using Disjoint Community Detection Algorithms. In: Fortunato, S., Mangioni, G., Menezes, R., Nicosia, V. (eds.) Complex Networks. SCI, vol. 207, pp. 47–61. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  20. Haynes, J., Perisic, I.: Mapping search relevance to social networks. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis - SNA-KDD 2009, vol. 9, pp. 1–7 (2009), doi:10.1145/1731011.1731013

    Google Scholar 

  21. Horowitz, D., Kamvar, S.D.: The anatomy of a large-scale social search engine. In: Proceedings of the 19th International Conference on World Wide Web - WWW 2010, p. 431 (2010), doi:10.1145/1772690.1772735

    Google Scholar 

  22. Index, P.P.: Content term extraction using pos tagging (2011), http://pypi.python.org/pypi/topia.termextract/

  23. Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2009, p. 467 (2009), doi:10.1145/1557019.1557074

    Google Scholar 

  24. Marsden, P.: Egocentric and sociocentric measures of network centrality. Social Networks 24(4), 407–422 (2002)

    Article  Google Scholar 

  25. Mike Cassidy, M.K.: An update to google social search (February 17, 2011), http://googleblog.blogspot.com/2011/02/update-to-google-social-search.html

  26. Mislove, A., Gummadi, K., Druschel, P.: Exploiting social networks for internet search. In: 5th Workshop on Hot Topics in Networks (HotNets 2006), p. 79. Citeseer (2006)

    Google Scholar 

  27. Network, Y.D.: Term extraction documentation for yahoo! search (2011), http://developer.yahoo.com/search/content/V1/termExtraction.html

  28. Newman, M.: Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 321–330 (2004)

    Article  Google Scholar 

  29. Newman, M.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577 (2006)

    Article  Google Scholar 

  30. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026,113 (2004), doi:10.1103/PhysRevE.69.026113

    Google Scholar 

  31. Palla, G., Barabási, A., Vicsek, T.: Quantifying social group evolution. Nature-London 446(7136), 664 (2007)

    Article  Google Scholar 

  32. Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814 (2005)

    Article  Google Scholar 

  33. Plangprasopchok, A., Lerman, K.: Exploiting social annotation for automatic resource discovery. In: AAAI Workshop on Information Integration from the Web (2007)

    Google Scholar 

  34. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proceedings of the National Academy of Sciences 101(9), 2658 (2004)

    Article  Google Scholar 

  35. Sabidussi, G.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966), http://www.springerlink.com/index/10.1007/BF02289527

    Article  MathSciNet  MATH  Google Scholar 

  36. Tyler, J., Wilkinson, D., Huberman, B.: Email as spectroscopy: Automated discovery of community structure within organizations. In: First International Conference on Communities and Technologies (2003)

    Google Scholar 

  37. Carey, V., Long, L., Gentleman, R.: Package rbgl (2011), http://cran.r-project.org/web/packages/RBGL/RBGL.pdf

  38. Wasserman, S., Faust, K.: Social network analysis: Methods and applications. Cambridge university press (1994)

    Google Scholar 

  39. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998), http://dx.doi.org/10.1038/30918

    Article  Google Scholar 

  40. Wingfield, N.: Facebook, microsoft deepen search ties (May 16, 2011), http://online.wsj.com/article/SB10001424052748703421204576327600877796140.html

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Correspondence to Prantik Bhattacharyya .

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Bhattacharyya, P., Wu, S.F. (2014). InfoSearch: A Social Search Engine. In: Chu, W. (eds) Data Mining and Knowledge Discovery for Big Data. Studies in Big Data, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40837-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-40837-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40836-6

  • Online ISBN: 978-3-642-40837-3

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