Abstract
Social networks have an evolving characteristic due to the continuous interaction between users, with nodes associating and disassociating with each other as time flies. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the one-pass constraint of data streams. Such evolving behavior leads to changes in the network topology that can be investigated under different perspectives. In this work we focus on the analysis of nodes position evolution—a node-centric perspective. Our goal is to spot change-points in an evolving network at which a node deviates from its normal behavior. Therefore, we propose a change detection model for processing evolving network streams which employs three different aggregating mechanisms for tracking the evolution of centrality metrics of a node. Our model is space and time efficient with memory less mechanisms and in other mechanisms at most we require the network of current time step T only. Additionally, we also compare the influence on different centralities’ fluctuations by the dynamics of real-world preferences. Consecutively, we apply our model in the user preference change detection task, reaching competitive levels of accuracy on Twitter network.
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
Notes
- 1.
- 2.
- 3.
Quoted-status are retweets with comments.
References
Abbasi, M.A., Tang, J., Liu, H.: Scalable learning of users’ preferences using networked data. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT ’14, pp. 4–12. ACM, New York, NY (2014)
Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: 12th SIAM International Conference on Data Mining, pp. 624–635 (2012)
Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29(3), 626–688 (2015)
Bifet, A., Holmes, G., Pfahringer, B., Gavaldà, R.: Mining frequent closed graphs on evolving data streams. In: 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’11, pp. 591–599 (2011)
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)
Buntain, C., Lin, J.: Burst detection in social media streams for tracking interest profiles in real time. In: 39th International ACM SIGIR Conference (2016)
Cordeiro, M., Gama, J.: Online Social Networks Event Detection: A Survey, pp. 1–41. Springer International Publishing, Cham (2016)
Fairbanks, J., Ediger, D., McColl, R., Bader, D.A., Gilbert, E.: A statistical framework for streaming graph analysis. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’13, pp. 341–347 (2013)
Gama, J.: Knowledge Discovery from Data Streams. Chapman & Hall/CRC, Boca Raton (2010)
Gama, J., Sebastião, R., Rodrigues, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2013)
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)
IDÉ, T., KASHIMA, H.: Eigenspace-based anomaly detection in computer systems. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pp. 440–449 (2004)
Li, J., Ritter, A., Jurafsky, D.: Inferring user preferences by probabilistic logical reasoning over social networks. CoRR (2014). http://arxiv.org/abs/1411.2679
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)
Mouss, H., Mouss, D., Mouss, N., Sefouhi, L.: Test of page-hinckley, an approach for fault detection in an agro-alimentary production system. In: Control Conference, 2004, 5th Asian, vol. 2, pp. 815–818. IEEE, New York (2004)
Page, E.S.: Continuous inspection schemes. Biometrika 41(1/2), 100–115 (1954)
Pereira, F.S.F., de Amo, S., Gama, J.: Detecting events in evolving social networks through node centrality analysis. Workshop on Large-scale Learning from Data Streams in Evolving Environments Co-located with ECML/PKDD (2016)
Pereira, F.S.F., de Amo, S., Gama, J.: On Using Temporal Networks to Analyze User Preferences Dynamics, pp. 408–423. Springer International Publishing, Cham (2016)
Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdiscip. Rev. Comput. Stat. 7(3), 223–247 (2015)
Rozenshtein, P., Anagnostopoulos, A., Gionis, A., Tatti, N.: Event detection in activity networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pp. 1176–1185 (2014)
Sebastião, R., Silva, M.M., Rabiço, R., Gama, J., Mendonça, T.: Real-time algorithm for changes detection in depth of anesthesia signals. Evol. Syst. 4(1), 3–12 (2013)
Wei, W., Carley, K.M.: Measuring temporal patterns in dynamic social networks. ACM Trans. Knowl. Discov. Data (TKDD) 10(1), 9 (2015)
Acknowledgements
This work was supported by the research project “TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020,” financed by the North Portugal Regional Operational Programme (NORTE 2020). This work was also supported by the Brazilian Research Agencies CAPES, CNPq, and Fapemig.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Pereira, F.S.F., Tabassum, S., Gama, J., de Amo, S., Oliveira, G.M.B. (2019). Processing Evolving Social Networks for Change Detection Based on Centrality Measures. In: Sayed-Mouchaweh, M. (eds) Learning from Data Streams in Evolving Environments. Studies in Big Data, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-89803-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-89803-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-89802-5
Online ISBN: 978-3-319-89803-2
eBook Packages: EngineeringEngineering (R0)