Abstract
To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for vertex attributes whose value changes impact the topology of the graph. In several applications, it appears that the variations of a group of attributes are often followed by some structural changes in the graph that one may assume they generate. We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis. We apply our approach on three real-world dynamic graphs of different natures—a co-authoring network, an airline network, and a social bookmarking system—assessing the relevancy of the triggering pattern mining approach.
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Notes
The term growth rate may be misleading: it is not related to time, but to the appearance of a target attribute in a sub group of objects with respect to the rest of the database.
This enables to avoid to consider any prefix sequence \(s'\) having an equivalent projected database than a sequence s discovered before, i.e., \({\Delta }_{|s} = {\Delta }_{|s'}\). Two cases are possible. Either \(s' \prec s\) (backward sub-pattern) or \(s \prec s'\) (backward super-pattern). In case of backward sub-pattern, the exploration of \(s'\) and its descendants is stopped. In case of backward super-pattern, the descendant of s are transplanted to \(s'\) instead of exploring an already scanned projected database.
See materials at: http://liris.cnrs.fr/~mplantev/doku/doku.php?id=trigat.
Measures computed with SNAP http://snap.stanford.edu/.
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Acknowledgments
This work has been partially supported by the project GRAISearch—EU Marie Curie Actions—FP7-PEOPLE-2013-IAPP.
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Kaytoue, M., Pitarch, Y., Plantevit, M. et al. What effects topological changes in dynamic graphs?. Soc. Netw. Anal. Min. 5, 55 (2015). https://doi.org/10.1007/s13278-015-0294-9
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DOI: https://doi.org/10.1007/s13278-015-0294-9