Anomaly Detection in Social Media Using Text-Mining and Emotion Classification with Emotion Detection

Bakkialakshmi, V. S.; SudalaiMuthu, T

doi:10.1007/978-3-031-22405-8_5

Cited by 7 publications

(1 citation statement)

References 36 publications

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“…With the advancement of science and technology, numerous domains, such as network security, intelligent transportation systems, social media, and computational biology [1][2][3][4] are producing large amounts of network-structured data composed of many interdependent objects and time-varying components. However, in these data, there are often anomalies (some unusual patterns or behaviors that significantly deviate from most of the data) that are typically associated with network attacks in network security and network fraud in social networks.…”

Section: Introductionmentioning

confidence: 99%

AddAG-AE: Anomaly Detection in Dynamic Attributed Graph Based on Graph Attention Network and LSTM Autoencoder

Miao

Zhang

et al. 2023

Electronics

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Recently, anomaly detection in dynamic networks has received increased attention due to massive network-structured data arising in many fields, such as network security, intelligent transportation systems, and computational biology. However, many existing methods in this area fail to fully leverage all available information from dynamic networks. Additionally, most of these methods are supervised or semi-supervised algorithms that require labeled data, which may not always be feasible in real-world scenarios. In this paper, we propose AddAG-AE, a general dynamic graph anomaly-detection framework that can fuse node attributes and spatiotemporal information to detect anomalies in an unsupervised manner. The framework consists of two main components. The first component is a feature extractor composed of a dual autoencoder, which captures a joint representation of both the network structure and node attributes in a latent space. The second component is an anomaly detector that combines a Long Short-Term Memory AutoEncoder (LSTM-AE) and a predictor, effectively identifying abnormal snapshots among most normal graph snapshots. Compared with baselines, experimental results show that the method proposed has broad applicability and higher robustness on three datasets with different sparsity.

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Section: Introductionmentioning

confidence: 99%