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.
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Acknowledgments
Research work supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “1st Call for H.F.R.I. Research Projects to Support Faculty Members & Researchers and Procure High-Value Research Equipment” (Project Number: HFRI-FM17-1873, GraphTempo).
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Tsoukanara, E., Koloniari, G., Pitoura, E. (2023). Skyline-Based Temporal Graph Exploration. In: Abelló, A., Vassiliadis, P., Romero, O., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2023. Lecture Notes in Computer Science, vol 13985. Springer, Cham. https://doi.org/10.1007/978-3-031-42914-9_7
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