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Erschienen in:
Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

SignedS2V: Structural Embedding Method for Signed Networks

verfasst von : Shu Liu, Fujio Toriumi, Xin Zeng, Mao Nishiguchi, Kenta Nakai

Erschienen in: Complex Networks and Their Applications XI

Verlag: Springer International Publishing

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Abstract

A signed network is widely observed and constructed from the real world and is superior for containing rich information about the signs of edges. Several embedding methods have been proposed for signed networks. Current methods mainly focus on proximity similarity and the fulfillment of social psychological theories. However, no signed network embedding method has focused on structural similarity. Therefore, in this research, we propose a novel notion of degree in signed networks and a distance function to measure the similarity between two complex degrees and a node-embedding method based on structural similarity. Experiments on five network topologies, an inverted karate club network, and three real networks demonstrate that our proposed method embeds nodes with similar structural features close together and shows the superiority of a link sign prediction task from embeddings compared with the state-of-the-art methods.

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Vorheriges Kapitel Using the Duplication-Divergence Network Model to Predict Protein-Protein Interactions
Nächstes Kapitel HM-LDM: A Hybrid-Membership Latent Distance Model
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Metadaten
Titel
SignedS2V: Structural Embedding Method for Signed Networks
verfasst von
Shu Liu
Fujio Toriumi
Xin Zeng
Mao Nishiguchi
Kenta Nakai
Copyright-Jahr
2023
Buch
Complex Networks and Their Applications XI

Print ISBN: 978-3-031-21126-3

Electronic ISBN: 978-3-031-21127-0

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-21127-0_28

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