To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for 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.
Name | Description | #V | #A | #T | Links |
RITA1 | US domestic flights in September 2001 | 220 | 8 | 30 | Attributes, Graph, Vertex Mapping, Attribute Mapping |
RITA2 | US domestic flights two years around 09/11 | 234 | 8 | 24 | Attributes, Graph, Vertex Mapping, Attribute Mapping |
RITA3 | Katrina Hurricane | 280 | 6 | 8 | Attributes, Graph, Vertex Mapping, Attribute Mapping |
DBLP | Co-authorship network | 2723 | 45 | 9 | Attributes, graph, and mappings |
Del.icio.us | Social bookmarking network | 1867 | 121 | 5 | Attributes, graph and mappings |