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2021 | OriginalPaper | Chapter

A Semi-supervised Learning Approach for Complex Information Networks

Authors : Paraskevas Koukaras, Christos Berberidis, Christos Tjortjis

Published in: Intelligent Data Communication Technologies and Internet of Things

Publisher: Springer Singapore

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Abstract

Information Networks (INs) are abstract representations of real-world interactions among different entities. This paper focuses on a special type of Information Networks, namely Heterogeneous Information Networks (HINs). First, it presents a concise review of the recent work in this field. Then, it proposes a novel method for querying such networks, using a bi-functional machine learning algorithm for clustering and ranking. It performs and elaborates on supervised and unsupervised, proof-of-concept modeling experiments on multi-typed, interconnected data while retaining their semantic importance. The results show that this method yields promising results and can be extended and utilized, using larger, real-world datasets.

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next chapter Tackling Counterfeit Medicine Through an Automated System Integrated with QR Code
Footnotes
1
Figure 2 presents six Influencers, seven SM networks, and four Topics. There are nine Influencer-Topic links and 14 Influencer-SM links.
 
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Metadata
Title
A Semi-supervised Learning Approach for Complex Information Networks
Authors
Paraskevas Koukaras
Christos Berberidis
Christos Tjortjis
Copyright Year
2021
Publisher
Springer Singapore
Book
Intelligent Data Communication Technologies and Internet of Things

Print ISBN: 978-981-15-9508-0

Electronic ISBN: 978-981-15-9509-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-9509-7_1