What effects topological changes in dynamic graphs?
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 naturesa co-authoring network, an airline network, and a social bookmarking systemassessing the relevancy of the triggering pattern mining approach.
dynamic graphs data mining mining methods and analysis attributed graph mining topological patterns