Abstract
Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.
This paper proposes an approach for (relative) community assessment. We introduce a set of so-called evidence networks which are capturing typical interactions in social network applications. Thus, we are able to apply a rich set of implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented approach applying user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.
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References
Agresti, A.: An Introduction to Categorical Data Analysis. Wiley-Blackwell, Chichester (2007)
Almendral, J.A., Oliveira, J., López, L., Mendes, J., Sanjuán, M.A.: The Network of Scientific Collaborations within the European Framework Programme. Physica A: Stat. Mech. and its Applic. 384(2), 675–683 (2007)
Atzmueller, M., Lemmerich, F., Krause, B., Hotho, A.: Who are the Spammers? Understandable Local Patterns for Concept Description. In: Proc. 7th Conference on Computer Methods and Systems (2009)
Baeza-Yates, R., Tiberi, A.: Extracting Semantic Relations from Query Logs. In: Proc. 13th ACM SIGKDD Conference, p. 85. ACM, New York (2007)
Benz, D., Hotho, A., Jäschke, R., Krause, B., Mitzlaff, F., Schmitz, C., Stumme, G.: The Social Bookmark and Publication Management System Bibsonomy. The VLDB Journal 19(6), 849–875 (2010)
Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is Nearest Neighbor Meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graph Structure in the Web. Computer Networks 33(1-6), 309–320 (2000)
Cattuto, C., Schmitz, C., Baldassarri, A., Servedio, V., Loreto, V., Hotho, A., Grahl, M., Stumme, G.: Network Properties of Folksonomies. AI Communications 20(4), 245–262 (2007)
Chung, F.R.K.: Spectral Graph Theory. CBMS Reg. Conf. Series in Mathematics, vol. 92 (1997)
Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-Law Distributions in Empirical Data. SIAM Review 51(4) (2009)
Crandall, D.J., Cosley, D., Huttenlocher, D.P., Kleinberg, J.M., Suri, S.: Feedback Effects between Similarity and Social Influence in Online Communities. In: Proc. 14th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pp. 160–168. ACM, New York (2008)
Diestel, R.: Graph Theory. Springer, Berlin (2006)
Fortunato, S., Castellano, C.: Community Structure in Graphs, arxiv:0712.2716 Chapter of Springer’s Encyclopedia of Complexity and System Science (2007)
Gaertler, M.: Clustering. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 178–215. Springer, Heidelberg (2005)
Karamolegkos, P.N., Patrikakis, C.Z., Doulamis, N.D., Vlacheas, P.T., Nikolakopoulos, I.G.: An Evaluation Study of Clustering Algorithms in the Scope of User Communities Assessment. Computers & Mathematics with Applications 58(8), 1498–1519 (2009)
Kashoob, S., Caverlee, J., Kamath, K.: Community-Based Ranking of the Social Web. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (2010)
Krause, B., Jäschke, R., Hotho, A., Stumme, G.: Logsonomy - Social Information Retrieval with Logdata. In: Proc. 19th Conf. on Hypertext and Hypermedia, pp. 157–166. ACM, New York (2008)
Lancichinetti, A., Fortunato, S.: Community Detection Algorithms: A Comparative Analysis, arxiv:0908.1062 (2009)
Leicht, E.A., Newman, M.E.J.: Community Structure in Directed Networks. Phys. Rev. Lett. 100(11) (March 2008)
Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters, arxiv:0810.1355 (2008)
Leskovec, J., Lang, K.J., Mahoney, M.W.: Empirical Comparison of Algorithms for Network Community Detection, cite arxiv:1004.3539 (2010)
MacQueen, J.B.: Some Methods for Classification and Analysis of MultiVariate Observations. In: Cam, L.M.L., Neyman, J. (eds.) Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Mislove, A., Marcon, M., Gummadi, K., Druschel, P., Bhattacharjee, B.: Measurement and Analysis of Online Social Networks. In: 7th ACM SIGCOMM Conf. on Internet Measurement, p. 42. ACM, New York (2007)
Mitzlaff, F., Benz, D., Stumme, G., Hotho, A.: Visit me, Click me, be my Friend: An Analysis of Evidence Networks of User Relationships in BibSonomy. In: HT 2010: Proc. 21st ACM Conference on Hypertext and Hypermedia, pp. 265–270. ACM, New York (2010)
Newman, M.E., Girvan, M.: Finding and Evaluating Community Structure in Networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69(2), 1–15 (2004)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)
Newman, M.E.J.: Detecting Community Structure in Networks. Europ. Physical J. 38 (2004)
Newman, M., Park, J.: Why Social Networks are different from Other Types of Networks. Physical Review E 68(3), 36122 (2003)
Schifanella, R., Barrat, A., Cattuto, C., Markines, B., Menczer, F.: Folks in Folksonomies: Social Link Prediction from Shared Metadata. In: Proc. 3rd ACM Int’l Conf. on Web Search and Data Mining, pp. 271–280. ACM, New York (2010)
Siersdorfer, S., Sizov, S.: Social Recommender Systems for Web 2.0 Folksonomies. In: HT 2009: Proc. 20th ACM Conf. on Hypertext and Hypermedia, pp. 261–270. ACM, New York (2009)
Szomszor, M., Cattuto, C., Van den Broeck, W., Barrat, A., Alani, H.: Semantics, Sensors, and the Social Web: The Live Social Semantics Experiments. In: The Semantic Web: Research and Applications, pp. 196–210 (2010)
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Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G. (2011). Community Assessment Using Evidence Networks. In: Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. (eds) Analysis of Social Media and Ubiquitous Data. MUSE MSM 2010 2010. Lecture Notes in Computer Science(), vol 6904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23599-3_5
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DOI: https://doi.org/10.1007/978-3-642-23599-3_5
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