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A Targeted Retraining Scheme of Unsupervised Word Embeddings for Specific Supervised Tasks Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Mining Recurrent Patterns in a Dynamic Attributed Graph

Authors : Zhi Cheng, Frédéric Flouvat, Nazha Selmaoui-Folcher

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

A great number of applications require to analyze a single attributed graph that changes over time. This task is particularly complex because both graph structure and attributes associated with each node can change. In the present work, we focus on the discovery of recurrent patterns in such a graph. These patterns are sequences of subgraphs which represent recurring evolutions of nodes w.r.t. their attributes. Various constraints have been defined and an original algorithm has been developed. Experiments performed on synthetic and real-world datasets have demonstrated the interest of our approach and its scalability.

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Metadata
Title
Mining Recurrent Patterns in a Dynamic Attributed Graph
Authors
Zhi Cheng
Frédéric Flouvat
Nazha Selmaoui-Folcher
Copyright Year
2017
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-57528-5

Electronic ISBN: 978-3-319-57529-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-57529-2_49

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