Skyline-based Exploration of Temporal Property Graphs

In this paper, we focus on temporal property graphs, that is, property graphs whose labeled nodes and edges as well as the values of the properties associated with them may change with time. A key challenge in studying temporal graphs lies in detecting interesting events in their evolution, defined as time intervals of significant stability, growth, or shrinkage. To address this challenge, we build aggregated graphs, where nodes are grouped based on the values of their properties, and seek events at the aggregated level. To locate such events, we propose a novel approach based on unified evolution skylines. A unified evolution skyline assesses the significance of an event in conjunction with the duration of the interval in which the event occurs. Significance is measured by a set of counts, where each count refers to the number of graph elements that remain stable, are created, or deleted, for a specific property value. Lastly, we share experimental findings that highlight the efficiency and effectiveness of our approach.