Abstract
Co-authorship networks are examples of social networks, in which researchers are linked by their joint publications. Like many other instances of social networks, co-authorship networks contain rich sets of valuable data. In this paper, we propose a visual analytic tool, called SocialVis, to analyze and visualize these networks. In particular, SocialVis first applies frequent pattern mining to discover implicit, previously unknown and potential useful social information such as teams of multiple frequently collaborating researchers, their composition, and their collaboration frequency. SocialVis then uses a visual representation to present the mined social information so as to help users get a better understanding of the networks.
Chapter PDF
Similar content being viewed by others
Keywords
References
Adams, R., Gill, S.P.: Augmented cognition, universal access and social intelligence in the information society. In: Schmorrow, D.D., Reeves, L.M. (eds.) FAC 2007, HCII 2007. LNCS (LNAI), vol. 4565, pp. 231–240. Springer, Heidelberg (2007)
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: ACM SIGMOD 1993, pp. 207–216 (1993)
Benchettara, N., Kanawati, R., Rouveirol, C.: Supervised machine learning applied to link prediction in bipartite social networks. In: ASONAM 2010, pp. 326–330 (2010)
Biryukov, M.: Co-author network analysis in DBLP: classifying personal names. In: MCO 2008. CCIS, vol. 14, pp. 399–408. Springer, Heidelberg (2008)
Blanchard, J., Guillet, F., Briand, H.: Interactive visual exploration of association rules with rule-focusing methodology. KAIS 13(1), 43–75 (2007)
Carmichael, C.L., Leung, C.K.-S.: CloseViz: visualizing useful patterns. In: ACM UP 2010, pp. 17–26 (2010)
Carrington, P.J., Scott, J., Wasserman, S. (eds.): Models and Methods in Social Network Analysis. Cambridge University Press, Cambridge (2005)
Chi, E.H.: Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reason. In: ACM SIGMOD 2009, pp. 973–984 (2009)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: ACM WSDM 2010, pp. 241–250 (2010)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Han, Y., Zhou, B., Pei, J., Jia, Y.: Understanding importance of collaborations in co-authorship networks: a supportiveness analysis approach. In: SDM 2009, pp. 1111-1122 (2009)
Hansen, D.L., Shneiderman, B., Smith, M.A.: Analyzing Social Media Networks with NodeXL. Morgan Kaufmann, Burlington, MA (2011)
Lakshmanan, L.V.S., Leung, C.K.-S., Ng, R.T.: Efficient dynamic mining of constrained frequent sets. ACM TODS 28(4), 337–389 (2003)
Leung, C.K.-S., Carmichael, C.L.: FpVAT: a visual analytic tool for supporting frequent pattern mining. SIGKDD Explorations 11(2), 39–48 (2009)
Leung, C.K.-S., Carmichael, C.L.: Exploring social networks: a frequent pattern visualization approach. In: IEEE SocialCom 2010, pp. 419–424 (2010)
Leung, C.K.-S., Irani, P.P., Carmichael, C.L.: FIsViz: a frequent itemset visualizer. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 644–652. Springer, Heidelberg (2008)
Leung, C.K.-S., Irani, P.P., Carmichael, C.L.: WiFIsViz: effective visualization of frequent itemsets. In: IEEE ICDM 2008, pp. 875–880 (2008)
Leung, C.K.-S., Khan, Q.I., Li, Z., Hoque, T.: CanTree: a canonical-order tree for incremental frequent-pattern mining. KAIS 11(3), 287–311 (2007)
Lévy, P.: Toward a self-referential collective intelligence some philosophical background of the IEML research program. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 22–35. Springer, Heidelberg (2009)
Makanju, A., Brooks, S., Zincir-Heywood, A.N., Milios, E.E.: LogView: visualizing event log clusters. In: PST 2008, pp. 99–108 (2008)
Milani Fard, A., Ester, M.: Collaborative mining in multiple social networks data for criminal group discovery. In: IEEE SocialCom 2009, pp. 582–587 (2009)
Misue, K.: Visual analysis tool for bipartite networks. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 871–878. Springer, Heidelberg (2008)
Ugai, T., Aoyama, K.: Organization diagnosis tools based on social network analysis. In: Smith, M.J., Salvendy, G. (eds.) Human Interface 2009, Part I, HCII 2009. LNCS, vol. 5617, pp. 181–189. Springer, Heidelberg (2009)
van Ham, F., Schulz, H.-J., DiMicco, J.M.: Honeycomb: visual analysis of large scale social networks. In: Gross, T., Gulliksen, J., Kotzé, P., Oestreicher, L., Palanque, P., Prates, R.O., Winckler, M. (eds.) INTERACT 2009, Part II . LNCS, vol. 5727, pp. 429–442. Springer, Heidelberg (2009)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leung, C.KS., Carmichael, C.L., Teh, E.W. (2011). Visual Analytics of Social Networks: Mining and Visualizing Co-authorship Networks. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. Directing the Future of Adaptive Systems. FAC 2011. Lecture Notes in Computer Science(), vol 6780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21852-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-21852-1_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21851-4
Online ISBN: 978-3-642-21852-1
eBook Packages: Computer ScienceComputer Science (R0)