Abstract
In this paper we present a novel algorithm for forming communities in a graph representing social relations as they emerge from the use of services like Twitter. The main idea centers in the careful use of features to characterize the members in the community, and in the hypothesis that well formed communities are those that designate diversity in the features of the participating members.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: WSDM 2008, pp. 183–194 (2008)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of community hierarchies in large networks. Journal of Statistical Mechanics: Theory and Experiment P1000 (2008)
Brin, S., Page, L.: The PageRank Citation Ranking: Bringing Order to the Web. Stanford Digital Library (1998)
Carrington, P.J., Scott, J., Wasserman, S.: Models and Methods in Social Network Analysis. Cambridge University Press (2005)
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Girvan, M., Newman, M.E.J.: Community Structure in Social and Biological Networks. National Academy of Sciences 99(12), 7821–7826 (2002)
Jurczyk, P., Agichtein, E.: Discovering Authorities in Question Answer Communities by Using Link Analysis. In: CIKM, pp. 919–922 (2007)
Kernighan, B.W., Lin, S.: An Efficient Heuristic Procedure for Partitioning Graphs. The Bell System Technical Journal 49(1), 291–307 (1970)
Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. SODA 1998:668-677
Lancichinetti, A., Fortunato, S.: Community detection algorithms: A comparative analysis. Physical Review E80, 056117 (2009)
Langville, A.N., Meyer, C.D.: Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press (2006)
Leskovec, J., Lang, K.J., Mahoney, M.W.: Empirical Comparison of Algorithms for Network Community Detection. In: WWW 2010, pp. 631–640 (2010)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. EÂ 69, 066133 (2004)
Newman, M.E.J.: Networks: An Introduction. Oxford University Press (2010)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On Spectral Clustering: Analysis and an algorithm. In: NIPS, pp. 849–856 (2001)
Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: WSDM, pp. 45–54 (2011)
Scott, J.G.: Social Network Analysis: A Handbook. SAGE Publications Ltd (2000)
Shi, J., Malik, J.: Normalized Cuts and Image Segmentation, pp. 731–737 (1997)
Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: Finding Topic-sensitive Influential Twitterers. In: WSDM 2010, pp. 261–270 (2010)
Zhang, J., Ackerman, M.S., Adamic, L.A.: Expertise Networks in Online Communities: Structure and Algorithms. In: WWW 2007, pp. 221–230 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kafeza, E., Kanavos, A., Makris, C., Chiu, D. (2014). Identifying Personality-Based Communities in Social Networks. In: Parsons, J., Chiu, D. (eds) Advances in Conceptual Modeling. ER 2013. Lecture Notes in Computer Science, vol 8697. Springer, Cham. https://doi.org/10.1007/978-3-319-14139-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-14139-8_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14138-1
Online ISBN: 978-3-319-14139-8
eBook Packages: Computer ScienceComputer Science (R0)