Complex Patterns in Dynamic Attributed Graphs

In recent years, there has been huge growth in the amount of graph data generated from various sources. These types of data are often represented by vertices and edges in a graph with real-valued attributes, topological properties, and temporal information associated with the vertices. Until recently, most pattern mining techniques focus solely on vertex attributes, topological properties, or a combination of these in a static sense; mining attribute and topological changes simultaneously over time has largely been overlooked. In this work-in-progress paper, we propose to extend an existing state-of-the-art technique to mine for patterns in dynamic attributed graphs which appear to trigger changes in attribute values.