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Artificial Intelligence Review
Published in: Artificial Intelligence Review 4/2021

30-09-2020

Tensor decomposition for analysing time-evolving social networks: an overview

Authors: Sofia Fernandes, Hadi Fanaee-T, João Gama

Published in: Artificial Intelligence Review | Issue 4/2021

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Abstract

Social networks are becoming larger and more complex as new ways of collecting social interaction data arise (namely from online social networks, mobile devices sensors, ...). These networks are often large-scale and of high dimensionality. Therefore, dealing with such networks became a challenging task. An intuitive way to deal with this complexity is to resort to tensors. In this context, the application of tensor decomposition has proven its usefulness in modelling and mining these networks: it has not only been applied for exploratory analysis (thus allowing the discovery of interaction patterns), but also for more demanding and elaborated tasks such as community detection and link prediction. In this work, we provide an overview of the methods based on tensor decomposition for the purpose of analysing time-evolving social networks from various perspectives: from community detection, link prediction and anomaly/event detection to network summarization and visualization. In more detail, we discuss the ideas exploited to carry out each social network analysis task as well as its limitations in order to give a complete coverage of the topic.
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Metadata
Title
Tensor decomposition for analysing time-evolving social networks: an overview
Authors
Sofia Fernandes
Hadi Fanaee-T
João Gama
Publication date
30-09-2020
Publisher
Springer Netherlands
Published in
Artificial Intelligence Review / Issue 4/2021
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-020-09916-4

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