ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks

Pei, Yulong; Huang, Tianjin; Ipenburg, Werner van; Pechenizkiy, Mykola

doi:10.1109/dsaa53316.2021.9564233

Cited by 17 publications

(10 citation statements)

References 23 publications

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“…Thus, many skeleton-based gait recognition methods which construct graph structures with joints as vertices and bones as edges have been proposed. For example, Teepe et al [3] proposed GaitGraph, a gait recognition model based on the residual graph convolution (ResGCN) [31]. The model uses GCN to model the spatial information of skeletons, enabling the accuracy of the model-based gait recognition method close to that of the appearance-based process.…”

Section: Graph Convolution Networkmentioning

confidence: 99%

MCDGait: Multimodal Co-Learning Distillation Network with Spatial-Temporal Graph Reasoning for Gait Recognition in the Wild

Xiong,

Zou,

Tang

et al. 2024

Preprint

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Gait recognition in the wild has attracted the attention of the academic community. However, existing unimodal algorithms cannot achieve the same performance on in-the-wild datasets as in-the-lab datasets because unimodal data have many limitations in-the-wild environments. Therefore, we propose a multimodal approach combining silhouettes and skeletons and formulates the multimodal gait recognition problem as a multimodal co-learning problem. In particular, we propose a multimodal co-learning distillation network (MCDGait) that integrates two student networks processing unimodal data into a single teacher network. Based on the semantic consistency of different modalities and the paradigm of deep mutual learning, the performance of the entire network is continuously improved via the bidirectional knowledge distillation between the student and teacher networks. Inspired by the observation that specific body parts or joints exhibit unique motion characteristics and have linkage with other parts or joints during walking, we propose a spatial-temporal graph reasoning module (ST-GRM). This module represents the parts or joints as graph nodes and the motion linkages between them as edges. By utilizing dynamic graph generator, the module implicitly captures the dynamic changes of the human body. Based on the generated graphs, the independent spatial-temporal linkage feature of each part and the interactive spatial-temporal linkage feature are aggregated simultaneously. Extensive experiments conducted on two in-the-wild datasets demonstrate the state-of-the-art performance of the proposed method. The average rank-1 accuracy on datasets Gait3D and GREW is 50.90% and 58.06%, respectively. The source code can be obtained from https://github.com/BoyeXiong/MCDGait.

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Section: Graph Convolution Networkmentioning

confidence: 99%

MCDGait: Multimodal Co-Learning Distillation Network with Spatial-Temporal Graph Reasoning for Gait Recognition in the Wild

Xiong,

Zou,

Tang

et al. 2024

Preprint

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“…Because of their complex nature, these abnormalities are generally the most difficult to diagnose. The sequence of deviations is one of the signs of aggregate anomaly [19].…”

Section: Basic Conceptsmentioning

confidence: 99%

“…Abnormal behaviors are then identified based on deviations from the induced models. In [19], a basic technique for detecting outliers is presented that also reports the reasoning behind it. The authors have proposed a graph signal processing indicator for the Markov stability framework that is used in community recognition to identify the underlying anomaly.…”

Section: A Review Of Previous Studiesmentioning

confidence: 99%

“…This section reports the implementation details of the proposed approach. Keras [19] and Sklearn [27] have been used as frameworks for the implementation of the models. Google-Colab environment has also been used to implement the proposed method.…”

Section: Implementation and Analysismentioning

confidence: 99%

“…This dataset is a collection of 400 call records from Isla Del Sueño over a ten-day period in June 2006. The datasets used for calls are artificial; that is, a combination of computer-generated data and humans [19] are generated. Users with numbers 0, 1, 2, 3, 5, 200, 300, 306, 309, and 360 are considered to be anomalies.…”

Section: Vast-2008 Challenge Datasetmentioning

confidence: 99%

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A Hybrid Recurrent Neural Network Approach for Detecting Abnormal User Behavior in Social Networks

Ebrahimzadeh

Nazari

Feizi‐Derakhshi

et al. 2023

Preprint

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With the recent advances in Internet technology, various aspects of the human lifestyle are influenced by the digital world. Users join social networks to communicate and perform their social activities. In addition to the vast benefits, social networks may also negatively affect different dimensions of user activities. One of the major challenges in social networks is the security issues of the individual user activities since usually there is no mechanism to detect abnormal behavior of the users. On the other hand, due to the continuous expansion and dynamic nature of social networks, identifying anomalies and abnormal behaviors of users in such networks is of particular importance. The literature offers several methods to manage the challenge of the security breach of user activities in social networks. A thorough inspection of these methods shows that most of them focus on two important factors: selecting the appropriate feature and increasing the accuracy of detecting abnormal behaviors. The main innovation of this paper is the proposal of a new hybrid method based on recurrent neural networks to analyze the abnormal behavior of users and manage the two challenges mentioned above. Our motivation for using recurrent neural networks as the basis of the proposed method is the temporal nature of user activities in social networks and the reasonable performance of these methods in identifying abnormal behaviors, processing time-series data, and the ability to analyze relationships between them. In the proposed method, statistical methods are used for the initial labeling of data. In the next step, the CNN-BiLSTM hybrid method is applied to the data in order to re-label them, establish order between the features and thus reduce the error. Then the abnormal batches are identified using the K-means clustering algorithm. The experiments show that the proposed method provides more accurate results compared to other statistical and learning-based methods. The efficiency of the proposed method is also reported on the VAST Challenge 2008 and Enron-Email datasets for the F-score benchmark, 0.82 and 0.88, respectively.

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Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks

Huang

Pei

Menkovski

et al. 2023

Lecture Notes in Computer Science

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ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks

Cited by 17 publications

References 23 publications

MCDGait: Multimodal Co-Learning Distillation Network with Spatial-Temporal Graph Reasoning for Gait Recognition in the Wild

MCDGait: Multimodal Co-Learning Distillation Network with Spatial-Temporal Graph Reasoning for Gait Recognition in the Wild

A Hybrid Recurrent Neural Network Approach for Detecting Abnormal User Behavior in Social Networks

Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks

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