Synonyms
Glossary
- Dynamic (or volatile) network:
-
A network which structure changes over time, some nodes and edges may appear and disappear.
- Local property:
-
A metric that describes a topological aspect on the local neighborhood of a node.
- Structural role:
-
The structural position of a node in the network, characterized by its local properties.
- Transition pattern:
-
A typical role change, characterized by its time interval and by the origin and destination role.
Definition
Many networks are intrinsically dynamic and change over time. These networks can be very volatile, with a significant number of edges and nodes appearing and disappearing. The majority of the existing network mining methodologies are however geared toward a more static scenario, with a single graph describing the topology of the system being analyzed. There is still a need for measurements and tools that allow the temporal dimension on network analysis to be...
References
Berlingerio M, Bonchi F, Bringmann B, Gionis A (2009) Mining graph evolution rules. In: Machine learning and Knowledge discovery in databases. Springer, Berlin/New York, pp 115–130
Choobdar S, Silva F, Ribeiro P (2011) Network node label acquisition and tracking. In: Progress in artificial intelligence, 15th Portuguese conference on artificial intelligence – EPIA’11, LNAI 7026. Springer, Lisbon, pp 418–430
Choobdar S, Silva F, Ribeiro P (2012) Event detection in evolving networks. In: International conference on computational aspects of social networks (CASoN), 2012
Costa LF, Rodrigues FA, Travieso G, Boas PRV (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56:167–242
Costa L, Rodrigues F, Hilgetag C, Kaiser M (2009) Beyond the average: detecting global singular nodes from local features in complex networks. EPL (Europhys Lett) 87:18,008
Dorogovtsev, S. N., & Mendes, J. F. (2005). The shortest path to complex networks. arXiv preprint cond-mat/0404593
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174
Gleditsch K (2002) Expanded trade and GDP data. J Confl Resolut 46(5):712
Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: Advances in social networks analysis and mining (ASONAM), 2010 international conference on, IEEE, pp 176–183
Hartigan J, Wong M (1979) A k-means clustering algorithm. J R Stat Soc C 28(1):100–108
Henderson K, Gallagher B, Eliassi-Rad T, Tong H, Basu S, Akoglu L, Koutra D, Faloutsos C, Li L, Matsubara Y, et al (2012) Rolx: structural role extraction & mining in large graphs. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1231–1239
Huan J, Wang W, Prins J (2003) Efficient mining of frequent subgraphs in the presence of isomorphism. In: Proceedings of the third IEEE international conference on data mining, ICDM’03, p 549
Jin R, McCallen S, Almaas E (2007) Trend motif: a graph mining approach for analysis of dynamic complex networks. In: Data mining, 2007. ICDM 2007. Seventh IEEE international conference on, IEEE, pp 541–546
Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pp 177–187
Leskovec J, Backstrom L, Kumar R, Tomkins A (2008) Microscopic evolution of social networks. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 462–470
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031
Lin Y, Chi Y, Zhu S, Sundaram H, Tseng B (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th international conference on World Wide Web, ACM, pp 685–694
Macskassy S, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8:935–983
Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827
Newman ME (2001) Scientific collaboration networks. I Network construction and fundamental results. Phys Rev E Stat Nonlin Soft Matter Phys 64(1 Pt 2)
RouteViews (1997) University of oregon route views project. online data and reports. http://www.routeviews.org (accessed February 2013)
Rossi R, Gallagher B, Neville J, Henderson K (2012) Role-dynamics: fast mining of large dynamic networks. In: Proceedings of the 21st international conference companion on World Wide Web, pp 997–1006
Sugar C, James G (2003) Finding the number of clusters in a dataset. J Am Stat Assoc 98(463):750–763
Sun J, Tao D, Faloutsos C (2006) Beyond streams and graphs: dynamic tensor analysis. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 374–383
Tang L, Liu H, Zhang J, Nazeri Z (2008) Community evolution in dynamic multi-mode networks. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 677–685
Acknowledgments
This work is in part funded by the ERDF/COMPETE Programme and by FCT within project FCOMP-01-0124-FEDER-022701. Sarvenaz Choobdar is funded by an FCT Research Grant (SFRH/BD/72697/2010). Pedro Ribeiro is funded by an FCT Research Grant (SFRH/BPD/81695/2011).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this entry
Cite this entry
Choobdar, S., Ribeiro, P., Silva, F. (2017). Querying Volatile and Dynamic Networks. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_390-1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7163-9_390-1
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7163-9
Online ISBN: 978-1-4614-7163-9
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering