Weitere Kapitel dieses Buchs durch Wischen aufrufen
Social network analysis is one of the most recent areas of research which is being used to analyze behavior of a society, person and even to detect malicious activities. The information of time is very important while evaluating a social network and temporal information based analysis is being used in research to have better insight. Theories like similarity proximity, transitive closure and reciprocity are some well-known studies in this regard. Social networks are the representation of social relationships. It is quite natural to have a change in these relations with the passage of time. A longitudinal method is required to observe such changes. This research contributes to explore suitable parameters or features that can reflect the relationships between individual in network. Any foremost change in the values of these parameters can capture the change in network. In this paper we present a framework for extraction of parameters which can be used for temporal analysis of social networks. The proposed feature vector is based on the changes which are highlighted in a network on two consecutive time stamps using the differences in betweenness centrality, clustering coefficient and valued edges. This idea can further be used for detection of any specific change happening in a network.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Katz, L., & Proctor, C. H. (1959). The configuration of interpersonal relations in a group as a time-dependent stochastic process. Psychometrika, 24, 317–327. CrossRef
Newcomb, T. M. (1962). Student peer-group influence. In N. Sanford (Ed.), The American College : A psychological and social interpretation of the higher learning. New York: Wiley.
Singer, B., & Spilerman, S. (1976). The representation of social processes by Markov models. The American Journal of Sociology, 82(1), 1–54. CrossRef
Hansen, D., Shneiderman, B., Smith, M. A. (2010) Analyzing social media networks with NodeXL: Insights from a connected world. Morgan Kaufmann. Elsevier Publication.
Xiaoyan, F., Hong, S. H., Nikola, S. N., Shen, X., Wu, Y., Xu, K. (2007) Visualization and analysis of email networks, 2007 I.E. APVIS. 329302.
Hardin, J. S., Sarkis, G. (2015) Network analysis with the enron email corpus. Journal of Statistics Education, 23(2), 3arXiv:1410.2759 [stat.OT] 4 Aug 2015.
Gloor, P. A., Laubacher, R., Zhao, Y., Dynes, S. Temporal visualization and analysis of social networks, NAACSOS Conference, June 27–29, Pittsburgh PA, North American Association for Computational Social and Organizational Science.
Uddin, S., Piraveenan, M., Chung, K. K. S., Hossain, L. (2013) Topological analysis of longitudinal networks. Annual Hawaii International Conference on system sciences Proceedings IEEE Computer Society 2013.
Andresen, E., Bergman, A., Hallen, L. The role of email communication in strategic networks:patterns observed over time, 19th Annual IMP Conference.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. CrossRef
Leskovec, J., Kleinberg, J., Faloutsos, C. (2005) Graphs over time: Densification laws, shrinking diameters and possible explanations. In In KDD (pp. 177–187).
Joseph J. Pfeiffer, Jennifer Neville (2011). Methods to determine node centrality and clustering in graphs with uncertain structure. Report Number: 11–010 Pfeiffer.
- Detecting Change from Social Networks using Temporal Analysis of Email Data
Muhammad Usman Akram
Shoab Ahmad Khan
Neuer Inhalt/© ITandMEDIA