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Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2021

01.12.2021 | Original Article

DeepFriend: finding abnormal nodes in online social networks using dynamic deep learning

verfasst von: Putra Wanda, Huang J. Jie

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2021

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Abstract

Detection of Online Social Networks (OSN) anomalous nodes becomes increasingly essential to identify malicious activities. The abnormal nodes suspiciously construct improbable links to other benign accounts. Inspired by the significant achievements of deep learning in current computer vision problems, we propose DeepFriend as a novel supervised neural network to classify abnormal nodes using labeled link features dataset. This paper proposes a model to classify malicious vertices using nodes' link information by training extensive features with dynamic deep learning architecture. To construct dynamic deep learning, we present a generic function called WalkPool pooling to optimize our network performance. By demonstrating our model, we gain higher accuracy than standard learning algorithms in the abnormal nodes’ classification.
Vorheriger Artikel Social network node influence maximization method combined with degree discount and local node optimization
Nächster Artikel Creative social media use for Covid-19 prevention in Bangladesh: a structural equation modeling approach

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Metadaten
Titel
DeepFriend: finding abnormal nodes in online social networks using dynamic deep learning
verfasst von
Putra Wanda
Huang J. Jie
Publikationsdatum
01.12.2021
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2021
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-021-00742-2

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