Results 21 to 30 of about 1,708,308 (346)
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters [PDF]
International Conference on Information and Knowledge Management, 2020 Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations.
Yingtong Dou +5 more
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E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT [PDF]
IEEE/IFIP Network Operations and Management Symposium, 2021 This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which can leverage the inherent structure of graph-based data. Training and evaluation
Wai Weng Lo +4 more
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LGNN: a novel linear graph neural network algorithm
Frontiers in Computational Neuroscience, 2023 The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks.
Shujuan Cao +16 more
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Stochastic Graph Neural Networks [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
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Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting [PDF]
Knowledge Discovery and Data Mining, 2022 Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods.
Zezhi Shao +3 more
semanticscholar +1 more source
Learning the Network of Graphs for Graph Neural Networks
CoRR, 2022 Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, there are three issues when applying GNNs: graphs are unknown, nodes have noisy features, and graphs contain noisy connections. Aiming at solving these problems, we propose a new graph neural network named as GL-GNN.
Yixiang Shan +5 more
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Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting [PDF]
International Joint Conference on Artificial Intelligence, 2017 Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect ...
Ting Yu, Haoteng Yin, Zhanxing Zhu
semanticscholar +1 more source
TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification [PDF]
Information Sciences, 2023 Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different ...
Huaiyuan Liu +6 more
semanticscholar +1 more source
Survey of Knowledge Graph Reasoning Based on Representation Learning [PDF]
Jisuanji kexue, 2023 Knowledge graphs describe objective knowledge in the real world in a structured form,and are confronted with issues of completeness and newly-added knowledge.As an important means of complementing and updating knowledge graphs,know-ledge graph reasoning ...
LI Zhifei, ZHAO Yue, ZHANG Yan
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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks
2023 IEEE International Conference on Big Data (BigData), 2023 This paper is submitted and will be published on Big Data Conference 2023, Data-driven Science for Graphs: Algorithms, Architectures, and Application ...
Zheyi Qin +2 more
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