Abstract
In this work, we focus on social interactions in communities in order to detect events. There are several previous efforts for the event detection problem based on analyzing the change in the network structure in terms of the overall network features. However, in this work, event detection is considered as a problem of change detection in community structures. Particularly, communities extracted from communication network are focused on, and various versions of the community change detection methods are developed using different models. Furthermore, ensemble methods combining the change models are proposed and their event detection performances are analyzed, as well. Experiments conducted on benchmark data set show that community change can be used as an indicator of event, and ensemble model further improves the event detection performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
S. Afra, T. Ozyer, J. Rokne, Netdriller version 2: A powerful social network analysis tool, in 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (Nov 2018), pp. 1475–1480
R. Aktunc, I.H. Toroslu, M. Ozer, H. Davulcu, A dynamic modularity based community detection algorithm for large-scale networks: DSLM, in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. ASONAM ’15 (2015), pp. 1177–1183
R. Aktunc, I. Toroslu, P. Karagoz, Event detection by change tracking on community structure of temporal networks, in ASONAM (2018), pp. 928–931. https://doi.org/10.1109/ASONAM.2018.8508325
J. Allan, Topic detection and tracking, in Introduction to Topic Detection and Tracking (Kluwer Academic Publishers, Norwell, 2002), pp. 1–16. http://dl.acm.org/citation.cfm?id=772260.772262
F. Atefeh, W. Khreich, A survey of techniques for event detection in Twitter. Comput. Intell. 31(1), 132–164 (2015)
V. Blondel, J. Guillaume, R. Lambiotte, E. Mech, Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008)
Y. Dong, F. Pinelli, Y. Gkoufas, Z. Nabi, F. Calabrese, N.V. Chawla, Inferring unusual crowd events from mobile phone call detail records. CoRR abs/1504.03643 (2015)
N. Eagle, A. Pentland, D. Lazer, Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. 106, 15274–15278 (2009)
S. Fortunato, Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Q. Gong, Y. Chen, X. He, Z. Zhuang, T. Wang, H. Huang, X. Wang, X. Fu, Deepscan: exploiting deep learning for malicious account detection in location-based social networks. IEEE Commun. Mag. 56(11), 21–27 (2018)
M. Imran, C. Castillo, F. Diaz, S. Vieweg, Processing social media messages in mass emergency: survey summary, in Companion Proceedings of the Web Conference 2018 (2018), pp. 507–511
I.A. Karatepe, E. Zeydan, Anomaly detection in cellular network data using big data analytics, in European Wireless 2014; 20th European Wireless Conference (May 2014), pp. 1–5
M.E.J. Newman, Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006)
G.K. Orman, V. Labatut, H. Cherifi, Comparative evaluation of community detection algorithms: a topological approach. CoRR abs/1206.4987 (2012). http://dblp.uni-trier.de/db/journals/corr/corr1206.html#abs-1206-4987
O. Ozdikis, P. Senkul, H. Oguztuzun, Semantic expansion of tweet contents for enhanced event detection in Twitter, in International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2012), pp. 20–24
O. Ozdikis, P. Karagoz, H. Oğuztüzün, Incremental clustering with vector expansion for online event detection in microblogs. Soc. Netw. Anal. Min. 7(1), 56 (2017)
T. Ozyer, R. Alhajj (eds.), Machine Learning Techniques for Online Social Networks. Lecture Notes in Social Networks (Springer International Publishing, Cham, 2018)
S. Rayana, L. Akoglu, Less is more: building selective anomaly ensembles with application to event detection in temporal graphs, in SDM (2015)
S. Rayana, L. Akogli, Less is more: building selective anomaly ensembles. ACM Trans. Knowl. Discov. Data 10(4), 42:1–42:33 (2016)
J. Sankaranarayanan, H. Samet, B.E. Teitler, M.D. Lieberman, J. Sperling, TwitterStand: news in tweets, in ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS) (2009), pp. 42–51
L. Tang, H. Liu, Community Detection and Mining in Social Media. Synthesis Lectures on Data Mining and Knowledge Discovery (Morgan and Claypool Publishers, San Rafael, 2010). https://doi.org/10.2200/S00298ED1V01Y201009DMK003
V.A. Traag, A. Browet, F. Calabrese, F. Morlot, Social event detection in massive mobile phone data using probabilistic location inference, in 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing (Oct 2011), pp. 625–628
L. Waltman, N.J. van Eck, A smart local moving algorithm for large-scale modularity-based community detection. CoRR abs/1308.6604 (2013). http://dblp.uni-trier.de/db/journals/corr/corr1308.html#WaltmanE13
M. Yu, Q. Huang, H. Qin, C. Scheele, C. Yang, Deep learning for real-time social media text classification for situation awareness – using hurricanes Sandy, Harvey, and Irma as case studies. Int. J. Digital Earth 0(0), 1–18 (2019)
Z. Zhang, Q. He, J. Gao, M. Ni, A deep learning approach for detecting traffic accidents from social media data. Transp. Res C Emerg. Technol. 86, 580–596 (2018)
X. Zhou, L. Chen, Event detection over Twitter social media streams. VLDB J. 23(3), 381–400 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Aktunc, R., Toroslu, I.H., Karagoz, P. (2020). Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks. In: Kaya, M., Birinci, Ş., Kawash, J., Alhajj, R. (eds) Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-33698-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-33698-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33697-4
Online ISBN: 978-3-030-33698-1
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)