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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

Vertex Entropy Based Link Prediction in Unweighted and Weighted Complex Networks

verfasst von : Purushottam Kumar, Dolly Sharma

Erschienen in: Complex Networks & Their Applications X

Verlag: Springer International Publishing

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Abstract

Network science is a domain in which we focus on studying complex networks like social networks, chemical networks, computer networks, telecommunication networks, cognitive networks, semantic networks, and biological networks. In recent years, link prediction in complex networks has become an active research field because of its various real-world applications. In this paper, we present a novel algorithm for link prediction influenced by the concept of vertex entropy and ego networks. We used 12 real-world datasets to evaluate the performance of the novel algorithm. Results are compared with the 12 baseline algorithm based on 4 metrics AUC, Precision, Prediction-Power, and Precision@K. Experimental results show the effectiveness of the novel algorithm.

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Vorheriges Kapitel Link Predictability Classes in Complex Networks
Nächstes Kapitel Population Dynamics and Its Instability in a Hawk-Dove Game on the Network
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Metadaten
Titel
Vertex Entropy Based Link Prediction in Unweighted and Weighted Complex Networks
verfasst von
Purushottam Kumar
Dolly Sharma
Copyright-Jahr
2022
Buch
Complex Networks & Their Applications X

Print ISBN: 978-3-030-93408-8

Electronic ISBN: 978-3-030-93409-5

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-93409-5_33

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