Skyline-Based Temporal Graph Exploration

Evangelia Tsoukanara, Georgia Koloniari, Evaggelia Pitoura

Abstract: An important problem in studying temporal graphs is detecting interesting events in their evolution, defined as time intervals of significant stability, growth, or shrinkage. We consider graphs whose nodes have attributes, for example in a network between individuals, the attributes may correspond to demographics, such as gender. We build aggregated graphs where nodes are grouped based on the values of their attributes, and seek for events at the aggregated level, for example, time intervals of significant growth between individuals of the same gender. We propose a novel approach based on temporal graph skylines. A temporal graph skyline considers both the significance of the event (measured by the number of graph elements that remain stable, are created, or deleted) and the length of the interval when the event appears. We also present experimental results of the efficiency and effectiveness of our approach.