Mining Temporal Networks

Polina Rozenshtein*, Aristides Gionis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Networks (or graphs) are used to represent and analyze large datasets of objects and their relations. Naturally, real-world networks have a temporal component: for instance, interactions between objects have a timestamp and a duration. In this tutorial we present models and algorithms for mining temporal networks, i.e., network data with temporal information. We overview different models used to represent temporal networks. We highlight the main differences between static and temporal networks, and discuss the challenges arising from introducing the temporal dimension in the network representation. We present recent papers addressing the most well-studied problems in the setting of temporal networks, including computation of centrality measures, motif detection and counting, community detection and monitoring, event and anomaly detection, analysis of epidemic processes and influence spreading, network summarization, and structure prediction.

Original languageEnglish
Title of host publicationKDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Number of pages2
ISBN (Electronic)978-1-4503-6201-6
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Anchorage, United States
Duration: 4 Aug 20198 Aug 2019
Conference number: 25


ConferenceACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Abbreviated titleKDD
Country/TerritoryUnited States


  • data mining
  • graph mining
  • temporal networks


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