scholarly journals One-class graph neural networks for anomaly detection in attributed networks

2021 ◽  
Author(s):  
Xuhong Wang ◽  
Baihong Jin ◽  
Ying Du ◽  
Ping Cui ◽  
Yingshui Tan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Graph Neural Networks ◽  
Class Graph ◽  
Attributed Networks

scholarly journals Anomaly detection with convolutional Graph Neural Networks

2021 ◽  
Vol 2021 (8) ◽  
Author(s):  
Oliver Atkinson ◽  
Akanksha Bhardwaj ◽  
Christoph Englert ◽  
Vishal S. Ngairangbam ◽  
Michael Spannowsky
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Top Quarks ◽  
New Physics ◽  
Broad Applicability ◽  
Latent Space ◽  
Edge Features ◽  
Graph Neural Networks ◽  
Promising Avenue ◽  
Do So

Abstract We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based discriminators, we find that such setups provide a promising avenue to isolate new physics and competing SM signatures from sensitivity-limiting QCD jet contributions. We demonstrate the flexibility and broad applicability of this approach using examples of W bosons, top quarks, and exotic hadronically-decaying exotic scalar bosons.

scholarly journals Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

2021 ◽  
Author(s):  
Lei Cai ◽  
Zhengzhang Chen ◽  
Chen Luo ◽  
Jiaping Gui ◽  
Jingchao Ni ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Dynamic Graphs ◽  
Temporal Graph ◽  
Graph Neural Networks

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Conversational Emotion Recognition Using Self-Attention Mechanisms and Graph Neural Networks

2020 ◽  
Author(s):  
Zheng Lian ◽  
Jianhua Tao ◽  
Bin Liu ◽  
Jian Huang ◽  
Zhanlei Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Graph Neural Networks
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