Complex Patterns in Dynamic Attributed Graphs

Rina Singh, Jeffrey A. Graves, Douglas A. Talbert

Abstract: 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.