Glossary
- Dynamic networks:
-
Networks that change over time
- Temporal analysis on social networks:
-
Exploring the evolution of social networks over time
- Temporary link:
-
A link that vanishes over time
- Temporary community:
-
A community formed by a set of actors and temporal ties they share during a time window
- Viral marketing:
-
Techniques that use preexisting social networks and other technologies to get an increase in brand awareness or achieve other marketing objectives
- HIV:
-
Human Immunodeficiency Virus
- AIDS:
-
Acquired Immunodeficiency Syndrome
Definition
Online social media have a very large spread in the Internet and Web era due to their great impact on many societies and organizations. In fact, using social media may ease communication, marketing, customer services, and even back-end business processes. At the heart of this spectacular growth is the concept of social network that stems from the...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ‘06. Philadelphia, Pennsylvania, USA, pp 44–54
Bohlin L, Edler D, Lancichinetti A, Rosvall M (2014) Community detection and visualization of networks with the map equation framework, Chapter 1. In: Measuring scholarly impact: methods and practice. Springer International Publishing, Cham, pp 3–34
Bródka P, Saganowski S, Kazienko P (2013) Ged: the method for group evolution discovery in social networks. Soc Netw Anal Min 3(1):1–14
Cazabet R, Amblard F (2014) Dynamic community detection. In: Encyclopedia of social network analysis and mining. Springer, New York, pp 404–414
Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, KDD ‘08. Las Vegas, Nevada, USA, pp 160–168
De Meo P, Ferrara E, Fiumara G, Provetti A (2014) Mixing local and global information for community detection in large networks. J Comput Syst Sci 80(1):72–87
Fiedler M (1973) Algebraic connectivity of graphs. Czech Math J 23:298–305
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci U S A 99(12):7821–7826
Goldberg MK, Magdon-Ismail M, Thompson J (2012) Identifying long lived social communities using structural properties. In: ASONAM. Istanbul, Turkey, pp 647–653
Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519:97–125, 1108.1780
Karnstedt M, Hennessy T, Chan J, Hayes C (2010) Churn in social networks: a discussion boards case study. In: Proceedings of the 2010 I.E. second international conference on social computing, IEEE computer society, SOCIALCOM ‘10. Washington, DC, USA, pp 233–240
Kashoob S, Caverlee J (2012) Temporal dynamics of communities in social bookmarking systems. Soc Netw Anal Min 2(4):387–404
Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(1):291–307
Lehmann S (2014) Community detection, current and future research trends, Chapter 27. In: Encyclopedia of social network analysis and mining. Springer, New York, pp 214–220
Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data 1(1)
Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on World wide web. New York, NY, USA, pp 641–650
Li J, Wang X, Cui Y (2014) Uncovering the overlapping community structure of complex networks by maximal cliques. Phys A Stat Mech Appl 415:398–406
Li X, Wu B, Guo Q, Zeng X, Shi C (2015) Dynamic community detection algorithm based on incremental identification. In: 2015 I.E. international conference on data mining workshop (ICDMW). Atlantic City, New Jersey, USA, pp 900–907, https://doi.org/10.1109/ICDMW.2015.158
Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT Press, Cambridge, MA
Masson MH, Denoeux T (2006) Inferring a possibility distribution from empirical data. Fuzzy Sets Syst 157(3):319–340
Matsuo Y, Mori J, Hamasaki M, Ishida K, Nishimura T, Takeda H, Hasida K, Ishizuka M (2006) Polyphonet: an advanced social network extraction system from the web. In: Proceedings of the 15th international conference on World Wide Web, ACM. Edinburgh, Scotland, pp 397–406
Newman MEJ (2004a) Detecting community structure in networks. Eur Phys J B 38(2):321–330
Newman MEJ (2004b) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066,133
Palla G, Barabasi AL, Vicsek T (2007) Quantifying social group evolution. Nature 446:664–667
Scott JP (2012) Social network analysis. Sage, London
Sun Y, Han J (2012) Mining heterogeneous information networks: principles and methodologies. Synthesis lectures on data mining and knowledge discovery. Morgan & Claypool Publishers, San Rafael, California, USA
Toivonen R, Kovanen L, Kivel M, Onnela JP, Saramki J, Kaski K (2009) A comparative study of social network models: network evolution models and nodal attribute models. Soc Networks 31(4):240–254
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media LLC, part of Springer Nature
About this entry
Cite this entry
Sarr, I., Missaoui, R. (2018). Temporal Analysis on Static and Dynamic Social Networks Topologies. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_387
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7131-2_387
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-7130-5
Online ISBN: 978-1-4939-7131-2
eBook Packages: Computer ScienceReference Module Computer Science and Engineering