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Hate Speech Detection on Social Media Using Graph Convolutional Networks Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Supervised Link Weight Prediction Using Node Metadata

Authors : Larissa Mori, Mario Ventresca, Toyya A. Pujol

Published in: Complex Networks & Their Applications X

Publisher: Springer International Publishing

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Abstract

Given that node metadata can provide key insights about the relationship between nodes, we investigate if incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link can improve the accuracy of weight prediction when using common supervised learning methods. We compare the weight prediction accuracy when metadata similarity is added to a set of baseline topological similarity features to that of using only the topological features. The comparison is performed across four empirical datasets using regression-based and other supervised methods found in the literature. In this preliminary study, we find no significant evidence that metadata similarity improves prediction accuracy in the methods analyzed and within the experimental setup. We encourage further investigation in this research area.

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previous chapter Graph-based Retrieval for Claim Verification over Cross-document Evidence
next chapter Joint Use of Node Attributes and Proximity for Node Classification
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Metadata
Title
Supervised Link Weight Prediction Using Node Metadata
Authors
Larissa Mori
Mario Ventresca
Toyya A. Pujol
Copyright Year
2022
Book
Complex Networks & Their Applications X

Print ISBN: 978-3-030-93412-5

Electronic ISBN: 978-3-030-93413-2

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-93413-2_42

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