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Node Classification Using Deep Learning in Social Networks Read chapter Read first chapter

2022 | OriginalPaper | Chapter

NN-LP-CF: Neural Network Based Link Prediction on Social Networks Using Centrality-Based Features

Authors : Shashank Sheshar Singh, Divya Srivastva, Ajay Kumar, Vishal Srivastava

Published in: Deep Learning for Social Media Data Analytics

Publisher: Springer International Publishing

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Abstract

Link prediction has drawn significant attention from researchers in recent years due to the rapid growth of social networks. The link prediction problem identifies the missing and future links in online social networks. Most traditional methods focus on the node and edge centrality measures to predict missing links using local and global features. These methods do not consider the advantage of both local and global features. However, some quasi-local metrics have been proposed to overcome the disadvantages of local and global centrality measures. This paper uses local and global topological features to generate likelihood feature scores for the proposed neural network model NN-LP-CF. The proposed model generates a likelihood score to predict missing links. The experimental results demonstrate the superiority of NN-LP-CF over traditional centrality measures over different performance metrics.

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Metadata
Title
NN-LP-CF: Neural Network Based Link Prediction on Social Networks Using Centrality-Based Features
Authors
Shashank Sheshar Singh
Divya Srivastva
Ajay Kumar
Vishal Srivastava
Copyright Year
2022
Book
Deep Learning for Social Media Data Analytics

Print ISBN: 978-3-031-10868-6

Electronic ISBN: 978-3-031-10869-3

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-10869-3_2

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