Abstract
There has been extensive research on social networks and methods for specific tasks such as: community detection, link prediction, and tracing information cascades; and a recent emphasis on using temporal dynamics of social networks to improve method performance. The underlying models are based on structural properties of the network, some of which we believe to be artifacts introduced from common misrepresentations of social networks. Specifically, representing a social network or series of social networks as an accumulation of network snapshots is problematic. In this paper, we use datasets with timestamped interactions to demonstrate how cumulative graphs differ from activity-based graphs and may introduce temporal artifacts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barabasi, A.-L.: The origin of bursts and heavy tails in human dynamics. Nature 435(7039), 207–211 (2005)
Barabâsi, A.-L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Phys. A Stat. Mech. Appl. 311(3), 590–614 (2002)
Gonçalves, B., Perra, N., Vespignani, A.: Modeling users activity on twitter networks: validation of dunbars number. PloS One 6(8), e22656 (2011)
Günnemann, S., Boden, B., Färber, I., Seidl,T.: Efficient mining of combined subspace and subgraph clusters in graphs with feature vectors. In: Advances in Knowledge Discovery and Data Mining, pp. 261–275. Springer (2013)
Hidalgo, C.A., Rodriguez-Sickert, C.: The dynamics of a mobile phone network. Phys. A Stat. Mech. Appl. 387(12), 3017–3024 (2008)
Holme, P., Saramäki, J.: Temporal networks. Phys. Rep. 519(3), 97–125 (2012)
Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Link Mining: Models, Algorithms, and Applications, pp. 337–357. Springer (2010)
Laurent, G., Saramäki, J., Karsai, M.: From calls to communities: a model for time varying social networks (2015). arXiv preprint arXiv:1506.00393
Leskovec, J.: Social media analytics: tracking, modeling and predicting the flow of information through networks. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 277–278. ACM (2011)
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 2 (2007)
Matsubara, Y., Sakurai, Y., Prakash, B.A., Li, L., Faloutsos, C.: Rise and fall patterns of information diffusion: model and implications. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 6–14. ACM (2012)
Miritello, G., Lara, R., Cebrian, M., Moro, E.: Limited communication capacity unveils strategies for human interaction. Sci. Rep. 3 (2013)
Miritello, G., Lara, R., Moro, E.: Time allocation in social networks: correlation between social structure and human communication dynamics. In: Temporal Networks, pp. 175–190. Springer (2013)
Miritello, G., Moro, E., Lara, R., Martínez-López, R., Belchamber, J., Roberts, S.G., Dunbar, R.I.: Time as a limited resource: communication strategy in mobile phone networks. Soc. Netw. 35(1), 89–95 (2013)
Moser, F., Colak, R., Rafiey, A., Ester, M.: Mining cohesive patterns from graphs with feature vectors. Proceedings of the SIAM International Conference on Data Mining (SIAM) 9, 593–604 (2009)
Perra, N., Gonçalves, B., Pastor-Satorras, R., Vespignani, A.: Activity driven modeling of time varying networks. Sci. Rep. 2 (2012)
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., et al.: Scratch: programming for all. Commun. ACM 52(11), 60–67 (2009)
Rivera, M.T., Soderstrom, S.B., Uzzi, B.: Dynamics of dyads in social networks: assortative, relational, and proximity mechanisms. Ann. Rev. Sociol. 36, 91–115 (2010)
Rossi, R., Neville, J.: Modeling the evolution of discussion topics and communication to improve relational classification. In: Proceedings of the First Workshop on Social Media Analytics, pp. 89–97. ACM (2010)
Rossi, R.A., Gallagher, B., Neville, J., Henderson, K.: Modeling dynamic behavior in large evolving graphs. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 667–676. ACM (2013)
Sun, Y., Tang, J., Han, J., Gupta, M., Zhao, B.: Community evolution detection in dynamic heterogeneous information networks. In: Proceedings of the Eighth Workshop on Mining and Learning with Graphs, pp. 137–146. ACM (2010)
Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 37–42. ACM (2009)
Yang, J., Leskovec, J.: Community-affiliation graph model for overlapping network community detection. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 1170–1175. IEEE (2012)
Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: IEEE 13th International Conference on Data Mining, pp. 1151–1156. IEEE (2013)
Acknowledgements
We appreciate the Lifelong Kindergarten group at MIT for publicly sharing the Scratch datasets. This work is partly based upon research supported by U.S. National Science Foundation (NSF) Awards DUE-1444277 and EEC-1408674. Any opinions, recommendations, findings, or conclusions expressed in this material are those of the authors and do not necessarily reflect the views of NSF.
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
Revelle, M., Domeniconi, C., Johri, A. (2019). Temporal Artifacts from Edge Accumulation in Social Interaction Networks. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-95098-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95097-6
Online ISBN: 978-3-319-95098-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)