Abstract
The increasing availability of data from online social networks attracts researchers’ interest, who seek to build algorithms and machine learning models to analyze users’ interactions and behaviors. Different methods have been developed to detect remarkable precursors preceding events, using text mining and Machine Learning techniques on documents, or using network topology with graph patterns.
Our approach aims at analyzing social networks data, through a graphlets enumeration algorithm, to identify event precursors and to study their contribution to the event. We test the proposed method on two different types of social network data sets: real-world events (Lubrizol fire, EU law discussion), and general events (Facebook and MathOverflow). We also contextualize the results by studying the position (orbit) of important nodes in the graphlets, which are assumed as event precursors. After analysis of the results, we show that some graphlets can be considered precursors of events.
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
Similar content being viewed by others
Notes
- 1.
- 2.
Implemented with the R package tseries: https://www.rdocumentation.org/packages/tseries/versions/0.1-2/topics/ccf.
- 3.
- 4.
The difference between the number of tweets in Sect. 4 and the number of nodes and edges is since several tweets can produce the same interaction.
References
Ackley, J.L., Puranik, T.G., Mavris, D.: A supervised learning approach for safety event precursor identification in commercial aviation. In: AIAA Aviation Forum, p. 2880 (2020)
Ansoff, H.I.: Managing strategic surprise by response to weak signals. Calif. Manage. Rev. 18(2), 21–33 (1975)
Baiesi, M.: Scaling and precursor motifs in earthquake networks. Phys. A 360(2), 534–542 (2006)
Brownlee, J.: Introduction to time series forecasting with python: how to prepare data and develop models to predict the future. Machine Learning Mastery (2017)
Coffman, B.: Weak signal research, part I: Introduction. J. Trans. Manag. 2(1) (1997)
Corcoran, W.R.: Defining and analyzing precursors. In: Accident precursor analysis and management: Reducing technological risk through diligence, pp. 79–88. National Academy Press Washington, DC (2004)
Davies, T., Marchione, E.: Event networks and the identification of crime pattern motifs. PloS one 10(11), e0143638 (2015)
Godet, M.: From anticipation to action: a handbook of strategic prospective. UNESCO publishing (1994)
Goldin, D.Q., Kanellakis, P.C.: On similarity queries for time-series data: constraint specification and implementation. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 137–153. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60299-2_9
Hiltunen, E.: Weak Signals in Organisational Futures. Aalto University School of Economics, Aalto (2010)
Hočevar, T., Demšar, J.: A combinatorial approach to Graphlet counting. Bioinf. 30(4), 559–565 (2014). https://doi.org/10.1093/bioinformatics/btt717
Hulovatyy, Y., Chen, H., Milenković, T.: Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics 31(12), i171–i180 (2015)
Juszczyszyn, K., Kołaczek, G.: Motif-based attack detection in network communication graphs. In: De Decker, B., Lapon, J., Naessens, V., Uhl, A. (eds.) CMS 2011. LNCS, vol. 7025, pp. 206–213. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24712-5_19
Krigsholm, R.: Applying text mining for identifying future signals of land administration. Land 8(12), 181 (2019). https://doi.org/10.3390/land8120181
Maitre, J., Ménard, M., Chiron, G., Bouju, A., Sidère, N.: A meaningful information extraction system for interactive analysis of documents. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 92–99. IEEE (2019)
Moreira, A.L.M., Hayashi, T.W.N., Coelho, G.P., da Silva, A.E.A.: A clustering method for weak signals to support anticipative intelligence. Int. J. Artif. Intell. Expert Syst. (IJAE) 6(1), 1–14 (2015)
Ning, Y., Muthiah, S., Rangwala, H., Ramakrishnan, N.: Modeling precursors for event forecasting via nested multi-instance learning. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1095–1104 (2016)
Pržulj, N.: Biological network comparison using GraphLet degree distribution. Bioinformatics 23(2), e177–e183 (2007)
Pržulj, N., Corneil, D.G., Jurisica, I.: Modeling interactome: scale-free or geometric? Bioinform. 20(18), 3508–3515 (2004)
Ray, S., McEvoy, D.S., Aaron, S., Hickman, T.T., Wright, A.: Using statistical anomaly detection models to find clinical decision support malfunctions. J. Am. Med. Inform. Assoc. 25(7), 862–871 (2018)
Ribeiro, P., Paredes, P., Silva, M.E., Aparicio, D., Silva, F.: A survey on subgraph counting: concepts, algorithms and applications to network motifs and graphlets. arXiv preprint arXiv:1910.13011 (2019)
Ripley, B.D., Venables, W.: Modern applied statistics with S. Springer (2002)
Welz, K., Brecht, L., Pengl, A., Kauffeldt, J.V., Schallmo, D.R.: Weak signals detection: criteria for social media monitoring tools. In: ISPIM Innovation Symposium, p. 1. The International Society for Professional Innovation Management (ISPIM) (2012)
Yoon, J.: Detecting weak signals for long-term business opportunities using text mining of web news. Expert Syst. Appl. 39(16), 12543–12550 (2012)
Acknowledgments
This work is supported by the program “Investissements d’Avenir”, ISITE-BFC project (ANR contract 15-IDEX-0003), https://projet-cocktail.fr/.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
�� 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Abou Jamra, H., Savonnet, M., Leclercq, É. (2021). Detection of Event Precursors in Social Networks: A Graphlet-Based Method. In: Cherfi, S., Perini, A., Nurcan, S. (eds) Research Challenges in Information Science. RCIS 2021. Lecture Notes in Business Information Processing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-75018-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-75018-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75017-6
Online ISBN: 978-3-030-75018-3
eBook Packages: Computer ScienceComputer Science (R0)