Anomaly Detection in Microblogging via Co-Clustering

Yang, Wu; Shen, Guowei; Wang, Wei; Gong, Liangyi; Yu, Ming; Dong, Guozhong

doi:10.1007/s11390-015-1585-3

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…These features are not directly visible like explicit features and require algorithms and analysis to reveal deeper characteristics of users, such as "posting patterns" and "correspondence between text and images". Yang et al [43] noticed that traditional user detection focuses on identifying individual anomalous users and proposed an anomaly detection framework based on bipartite graphs and co-clustering to identify abnormal users. However, due to the singular form of abnormal user samples, it is difficult to address anomaly detection in different social environments.…”

Section: Fake User Detectionmentioning

confidence: 99%

Fake User Detection Based on Multi-Model Joint Representation

Li,

Jiang,

Zhang

et al. 2024

Information

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The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by analyzing user behavior datasets within a time retrospective window. It integrates a pre-trained Domain Large Model to represent user behavior data across multiple modalities, thereby constructing a high-generalization implicit behavior feature spectrum for users. In response to the tendency of existing fake user behavior mining to neglect time-series features, this study introduces an improved network called Sequence Interval Detection Net (SIDN) based on Sequence to Sequence (seq2seq) to characterize time interval sequence behaviors, achieving strong expressive capabilities for detecting fake behaviors within the time window. Ultimately, the amalgamation of latent behavioral features and explicit characteristics serves as the input for spectral clustering in detecting fraudulent users. The experimental results on Weibo real dataset demonstrate that the proposed model outperforms the detection utilizing explicit user features, with an improvement of 27.0% in detection accuracy.

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Section: Fake User Detectionmentioning

confidence: 99%

Fake User Detection Based on Multi-Model Joint Representation

Li,

Jiang,

Zhang

et al. 2024

Information

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“…The use of matrix decomposition and matrix factorization techniques has been used for anomaly detection [9,10], but with large graphs they do not perform well. The detection of graph anomalies has recently been studied using deep learning architectures [11][12][13].…”

Section: Related Workmentioning

confidence: 99%

Robust Graph Neural-Network-Based Encoder for Node and Edge Deep Anomaly Detection on Attributed Networks

et al. 2023

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The task of identifying anomalous users on attributed social networks requires the detection of users whose profile attributes and network structure significantly differ from those of the majority of the reference profiles. GNN-based models are well-suited for addressing the challenge of integrating network structure and node attributes into the learning process because they can efficiently incorporate demographic data, activity patterns, and other relevant information. Aggregate operations, such as sum or mean pooling, are utilized by Graph Neural Networks (GNNs) to combine the representations of neighboring nodes within a graph. However, these aggregate operations can cause problems in detecting anomalous nodes. There are two main issues to consider when utilizing aggregate operations in GNNs. Firstly, the presence of anomalous neighboring nodes may affect the representation of normal nodes, leading to false positives. Secondly, anomalous nodes may be overlooked as their representation is flattened during the aggregate operation, leading to false negatives. The proposed approach, AnomEn, is a robust graph neural network developed for anomaly detection. It addresses the challenges of false positives and false negatives using a weighted aggregate mechanism. This mechanism is designed to differentiate between a node’s own features and the features of its neighbors by placing greater emphasis on a node’s own features and less emphasis on its neighbors’ features. The system can preserve the node’s original characteristics, whether the node is normal or anomalous. This work proposes not only a robust graph neural network, namely, AnomEn, but also specific anomaly detection structures for nodes and edges. The proposed AnomEn method serves as the encoder in the node and edge anomaly detection architectures and was tested on multiple datasets. Experiments were conducted to validate the effectiveness of the proposed method as a graph neural network encoder. The findings demonstrated the robustness of the proposed method in detecting anomalies. The proposed method outperforms other existing methods in node anomaly detection tasks by 5.63% and edge anomaly detection tasks by 7.87%.

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“…• Non-negative matrix tri-factorization-It is a framework proposed by Yang et al for detecting anomalies based on bipartite graph and co clustering algorithm. It mainly focuses on detecting anomalous behavior in micro blogging [Yang et al, (2015)]. Co-clustering is based on non negative matrix tri-factorization that can able to detect anomalous user and messages simultaneously.…”

Section: Algorithms For Anomaly Detection In Static Labeled Graphmentioning

confidence: 99%

Some Observation of Algorithms Developed for Anomaly Detection

Raj¹,

Garg²

2020

INDJCSE

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With the growth of social networks tremendous amount of data are generated in a regular interval of time that can be our search histories, likes, shares, posts, comments, etc. The generated data are publicly available which may become the prime target for the malicious users, who try to attack and harm the innocent users. Using anomaly detection techniques, we can identify the unusual behavior of such users. In social networks, anomalies can be detected by exploring the pattern hidden in the network. This paper mainly focuses on the graph mining techniques used for anomaly detection in social networks. The algorithm for anomaly detection using graph mining techniques has been categorized on the basis of different characteristics of anomalies, and the types of anomalies generated. In addition to this, the paper also emphasizes on the issues and challenges associated with each developed algorithm that will help the researchers and the scientists working in this area to find the solutions for problems associated with various algorithms.

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Anomaly Detection in Microblogging via Co-Clustering

Cited by 17 publications

References 28 publications

Fake User Detection Based on Multi-Model Joint Representation

Fake User Detection Based on Multi-Model Joint Representation

Robust Graph Neural-Network-Based Encoder for Node and Edge Deep Anomaly Detection on Attributed Networks

Some Observation of Algorithms Developed for Anomaly Detection

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