Mining evolving patterns in dynamic relational networks

Rezwan Ahmed, George Karypis

Research output: Chapter in Book/Report/Conference proceedingChapter


Dynamic networks have recently been recognized as a powerful abstraction to model and represent the temporal changes and dynamic aspects of the data underlying many complex systems. This recognition has resulted in a burst of research activity related to modeling, analyzing, and understanding the properties, characteristics, and evolution of such dynamic networks. The focus of this growing research has been on mainly defining important recurrent structural patterns and developing algorithms for their identification. Most of these tools are not designed to identify time-persistent relational patterns or do not focus on tracking the changes of these relational patterns over time. Analysis of temporal aspects of the entity relations in these networks can provide significant insight in determining the conserved relational patterns and the evolution of such patterns over time. In this chapter we present new data mining methods for analyzing the temporal evolution of relations between entities of relational networks. A qualitative analysis of the results shows that the discovered patterns are able to capture network characteristics that can be used as features for modeling the underlying dynamic network in the context of a classification task.

Original languageEnglish (US)
Title of host publicationUnsupervised Learning Algorithms
PublisherSpringer International Publishing
Number of pages48
ISBN (Electronic)9783319242118
ISBN (Print)9783319242095
StatePublished - Jan 1 2016

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.


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