On Compressing Temporal Graphs

Panagiotis Liakos, Katia Papakonstantinopoulou, Theodore Stefou, Alex Delis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Contemporary data-systems empowering the daily human activity are routinely represented with graphs. During the last decade, the volume growth of such systems has been unprece-dented. This hinders the timely analysis of the formed networks due to existing physical memory limitations and significant I/O overheads. Graph compression techniques have managed to reduce memory requirements and allow for representing such networks using a few bits-per-edge. Respective approaches offer succinct mappings for social, biological, and information networks while allowing for the efficient access of sought graph elements. Despite their success, such methods mostly focus on static graphs, and predominantly offer access to either a snapshot or an aggregated view of a network. In reality however, networks change over time and, in many instances, we are interested in capturing and studying this evolution. In this paper we propose a framework for compressing emerging temporal graphs based on a dual-representation which articulates both network structure and corresponding temporal information. We empirically establish properties exhibited by community-networks regarding their time aspect(s) and harness these features in our proposed repre-sentation. Our experimental evaluation demonstrates that our approach for compressing temporal graphs readily outperforms competing techniques, attaining compression ratios that are on average around 60% of the space required by state-of-the-art techniques. Moreover, our memory-efficient representation yields more than 70 % faster graph compression and orders of magnitude quicker retrieval of graphs' elements, especially when it comes to large-scale networks. Finally, our framework is the first effort we are aware of, that considers actual time instead of time steps. This helps us attain better control for the size of our representation and reap further memory savings.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 38th International Conference on Data Engineering, ICDE 2022
PublisherIEEE Computer Society
Number of pages13
ISBN (Electronic)9781665408837
StatePublished - 2022
Event38th IEEE International Conference on Data Engineering, ICDE 2022 - Virtual, Online, Malaysia
Duration: May 9 2022May 12 2022

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference38th IEEE International Conference on Data Engineering, ICDE 2022
CityVirtual, Online


  • evolving networks
  • graph compression
  • temporal graphs

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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