Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders

Keser, Reyhan Kevser; Nallbani, Indrit; Çalık, Nurullah; Ayanzadeh, Aydin; Tgreyin, Behcet Ugur

doi:10.1109/siu49456.2020.9302271

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…For example, ADN 24 is a graph autoencoder structure and achieves information diffusion through alternating spatial and temporal self-attention. Due to the power of GAE 25 , it is widely used in different research directions, such as link prediction [26][27][28][29][30] , graph clustering 31,32 , hyperspectral anomaly detection 33 . While the traditional GCN takes node features and adjacency matrix as input and node embedding as output, GAEs compresses the node embeddings of all nodes in a graph to a single graph embedding to obtain information about the context.…”

Section: Graph Convolution Networkmentioning

confidence: 99%

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Ren,

Li,

Peng

et al. 2024

Sci Rep

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Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.

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Section: Graph Convolution Networkmentioning

confidence: 99%

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Ren,

Li,

Peng

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Graph autoencoders (GAEs) are a kind of unsupervised learning method, which mean that they map nodes to a potential vector space by encoding process, reconstructing graph information with the information in the vector to generate a graph similar to the original one (decoding) 13,20 . Due to the power of GAE 11 , it is widely used in different research directions, such as link prediction [21][22][23][24][25] , graph clustering 26,27 , hyperspectral anomaly detection 28 . While the traditional GCN takes node features and adjacency matrix as input and node embedding as output, GAEs compresses the node embeddings of all nodes in a graph to a single graph embedding to obtain information about the context.…”

Section: Graph Convolution Networkmentioning

confidence: 99%

MTGAE: Graph Autoencoder with Mirror TCN for Traffic Anomaly Detection

Ren

et al. 2023

Preprint

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Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. Classical traffic anomaly detection methods ignore the hidden dynamic associations between road network nodes as it evolves, resulting in the inability to capture the long-term temporal correlation, spatial and temporal characteristics of traffic data, and the abnormal nodes from it in a dataset with high periodicity and trends (e.g., morning peak travel). In this paper, we proposed a Mirror Temporal Graph Autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. In the method, we propose Mirror Temporal Convolutional Module (MTCM) module, to our best knowledge, which possesses better feature extraction and captures hidden node-to-node features in the traffic network and will be passed as hidden to the next module. Our proposed Graph Convolutional Gate Recurrent Unit Cell (GCGRU CELL) module, mapped to a high-dimensional space by Gaussian kernel functions, which discerns anomalous information in the traffic network and possible anomalies in the complex interdependencies between nodes and nodes based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. The experimental results on the NYC dataset prove that our model works best compared to other models for traffic anomaly detection.

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“…Phuc, Yamada & Kashima (2020) embeds several graphs with similar structural properties to boost link prediction accuracy. Keser et al (2020) employs skip-connections in VGAE.…”

Section: Link Predictionmentioning

confidence: 99%

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

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Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

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Graph Embedding For Link Prediction Using Residual Variational Graph Autoencoders

Cited by 5 publications

References 12 publications

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

MTGAE: Graph Autoencoder with Mirror TCN for Traffic Anomaly Detection

Survey on graph embeddings and their applications to machine learning problems on graphs

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