Granularity of co-Evolution Patterns in Dynamic Attributed Graphs
Résumé
Many applications see huge demands for discovering relevant patterns
in dynamic attributed graphs, for instance in the context of social
interaction analysis. It is often possible to associate a hierarchy
on the attributes associated to graph vertices to explicit prior
knowledge. For example, considering the study of scientific
collaboration networks, conference venues and journals can be
grouped with respect to types or topics. We propose to extend a
recent constraint-based mining method by exploiting such hierarchies
on attributes. We define an algorithm that enumerates all
multi-level co-evolution sub-graphs, i.e., induced sub-graphs that
satisfy a topologic constraint and whose vertices follow the same
evolution on a set of attributes during some timestamps. Experiments
show that hierarchies make it possible to return more concise
collections of patterns without information loss in a feasible time.