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

Wanda P., Jie H.J.

Harbin University of Science and Technology, Harbin, China; Universitas Respati Yogyakarta, Yogyakarta, Indonesia


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. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

Abnormal nodes; Deep learning; Online social network; Social relation


Social Network Analysis and Mining

Publisher: Springer

Volume 11, Issue 1, Art No 34, Page – , Page Count

Journal Link:

doi: 10.1007/s13278-021-00742-2

Issn: 18695450



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