DNEA captures the dynamic characteristics of the network to reconstruct the network topology structure based on Stochastic Block Model (SBM), and detects anomalous edges from the perspective of reconstruction probability. In addition, DNEA utilizes negative sampling to handle the challenge of scarce anomaly labels.
Aug 7, 2021
Aug 14, 2021 · We propose a novel end-to-end dynamic network embedding method called Dynamic Network Embedding for Anomaly Detection (DNEA), which can learn the robust node ...
DNEA captures the dynamic characteristics of the network to reconstruct the network topology structure based on Stochastic Block Model (SBM), and detects ...
DNEA captures the dynamic characteristics of the network to reconstruct the network topology structure based on Stochastic Block Model (SBM), and detects ...
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May 8, 2024 · Self-supervised Dynamic Graph Embedding with evolutionary ... DNEA: Dynamic Network Embedding Method for Anomaly Detection. KSEM ...
Oct 21, 2023 · In this paper, we propose an end-to-end continuous-time model, named Temporal Heterogeneous Graph Neural Network (THGNN), to detect anomalous behaviors (edges) ...
Inspired by this, we propose a novel end-to-end dynamic network embedding method called Dynamic Network Embedding for Anomaly Detection (DNEA), which can learn ...
Mar 30, 2021 · . As labeled data is especially insufficient in anomaly detection, negative sampling strat- egy and margin loss technique are also applied.
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Yu, Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks, с. ... Zang, DNEA: Dynamic network embedding method for anomaly ...
In this paper, we propose a novel approach, NetWalk, for anomaly detection in dynamic networks by learning network representations which can be updated ...
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