Search Results - Attributed networks autoencoder

Anomalous node detection in attributed social networks using dual variational autoencoder with generative adversarial networks by Khan, Wasim, Abidin, Shafiqul, Arif, Mohammad, Ishrat, Mohammad, Haleem, Mohd, Shaikh, Anwar Ahamed, Farooqui, Nafees Akhtar, Faisal, Syed Mohd

“… Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account…”
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Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks by Fan, Haoyi, Zhang, Fengbin, Li, Zuoyong

“…Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network…”
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Anomaly Detection with Deep Graph Autoencoders on Attributed Networks by Zhu, Dali, Ma, Yuchen, Liu, Yinlong

“…Anomaly detection on attributed networks aims to differentiate rare nodes that are significantly different from the majority…”
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Deep autoencoder architecture with outliers for temporal attributed network embedding by Mo, Xian, Pang, Jun, Liu, Zhiming

“…Temporal attributed network embedding aspires to learn a low-dimensional vector representation for each node in each snapshot of a temporal network, which can be capable of various network analysis…”
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Learning graph deep autoencoder for anomaly detection in multi-attributed networks by Shao, Minglai, Lin, Yujie, Peng, Qiyao, Zhao, Jun, Pei, Zhan, Sun, Yueheng

“…Anomaly detection in multi-attributed networks has become increasingly important and has significant implications in various domains, such as intrusion detection, botnet detection, financial fraud…”
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Redundancy-aware masked graph autoencoder for overlapping community detection in attributed networks by Xie, Hongkai, Ying, Xinyi, Wang, Xiaofeng, Qi, Yuanyuan, Chen, Wei, Huang, Xiaofeng, Jiang, Junzheng, Quan, Daying

“…Overlapping community detection is a critical task in complex network analysis, especially for real-world graphs where nodes participate in multiple communities…”
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Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder by Al-Furas, Amr Thabit, Alrahmawy, Mohammed F., Al-Adrousy, Waleed Mohamed, Elmougy, Samir

“… In the present paper, a Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder (DANE-WLA…”
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Graph variational autoencoder with affinity propagation for community-aware anomaly detection in attributed networks by Cao, Zhijie, Yang, Chengkun, Fan, Xiaoqing, Li, Lingjie, Lin, Qiuzhen, Li, Jianqiang, Ma, Lijia

“…) for community-aware ADAN. GVE-AP first employs a graph convolutional variational autoencoder to learn node embeddings from attributed networks…”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Khan, Wasim, Haroon, Mohammad, Khan, Ahmad Neyaz, Hasan, Mohammad Kamrul, Khan, Asif, Mokhtar, Umi Asma, Islam, Shayla

“…A significant aspect of today's digital information is attributed networks, which combine multiple node attributes with the basic network topology to extract knowledge…”
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Unsupervised graph attention autoencoder clustering-oriented for community detection in attributed networks by Bekkair, Abdelfateh, Bellaouar, Slimane, Oulad-Naoui, Slimane

“…) for community detection in attributed networks. The model adeptly captures representations from both the network’s…”
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AddAG-AE: Anomaly Detection in Dynamic Attributed Graph Based on Graph Attention Network and LSTM Autoencoder by Miao, Gongxun, Wu, Guohua, Zhang, Zhen, Tong, Yongjie, Lu, Bing

“…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…”
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DVAE-GNN: a dual variational autoencoder graph neural network for unsupervised anomaly detection in static attributed networks by Piridi, Hari Prasad, Chakraborty, Dipanjan, Upadhyay, Prajna Devi

“…), a novel framework for unsupervised anomaly detection in static attributed networks leveraging the strengths of variational autoencoders (VAEs…”
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An autoencoder considering multi-order and structural-role similarity for community detection in attributed networks by Guo, Kun, Lin, Gaosheng, Wu, Ling

“…A community is composed of closely related nodes. Detecting communities in a network has many practical applications, such as online product recommendation, biological molecule discovery and criminal group tracking…”
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Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss by Bekkair, Abdelfateh, Bellaouar, Slimane, Oulad-Naoui, Slimane

“…}ncoder for community detection in attributed networks (GAECO). The proposed model adeptly learns representations from both the network's topology and attribute information, simultaneously addressing dual objectives…”
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A Comparative Study of Graph Autoencoder-based Community Detection in Attributed Networks by Bekkair, Abdelfateh, Bellaouar, Slimane, Oulad-Naoui, Slimane, Zita, Kaoutar

“… In this paper, we begin by proposing a taxonomy of graph autoencoder community detection approaches in attributed networks based on the type of autoencoder…”
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AttentionAE: Autoencoder for Anomaly Detection in Attributed Networks by Qin, Kenan, Zhou, Yihui, Tian, Bo, Wang, Rui

“… Therefore, in this paper, we propose a method for attributed network anomaly detection based on an autoencoder considering the node attention mechanism and an anomaly score generator…”
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STEAM: SVDD-based Anomaly Detection on Attributed Networks via Multi-Autoencoders by Zhang, Junjie, Wang, Xinye, Yu, Zhenyang, Huang, Menglin, Duan, Lei, Zuo, Jie

“…Anomaly detection on attributed networks is intended to find instances that dramatically different from other instances in terms of attributes or structure…”
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A Graph Autoencoder-based Anomaly Detection Method for Attributed Networks by Zhang, Kunpeng, Lu, Guangyue, Li, Yuxin, Xu, Cai

“… To address the above problems, we propose a graph autoencoder-based anomaly detection method for attributed networks…”
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Trans-DAE: Transformer-based Double Autoencoder for Anomaly Detection on Attributed Networks by Zhang, Kunpeng, Lu, Guangyue, Li, Yuxin

“…Anomaly node detection on attribute networks has recently attracted increasing research attention and has a wide range of applications in many fields, such as cyber security, finance, and healthcare…”
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Dual-decoder graph autoencoder for unsupervised graph representation learning by Sun, Dengdi, Li, Dashuang, Ding, Zhuanlian, Zhang, Xingyi, Tang, Jin

“… Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks…”
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