Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power
Giorgos Bouritsas +3 more
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ChatGPT Informed Graph Neural Network for Stock Movement Prediction [PDF]
Social Science Research Network, 2023 ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored ...
Zihan Chen +4 more
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Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
IEEE Access, 2021 Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also ...
Joakim Skarding +2 more
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Graph Neural Network-Based EEG Classification: A Survey [PDF]
IEEE transactions on neural systems and rehabilitation engineering, 2023 Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers.
D. Klepl, Min Wu, F. He
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BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis
bioRxiv, 2020 Understanding how certain brain regions relate to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research.
Xiaoxiao Li +10 more
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Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider both complex intra-variable dependencies and inter-variable dependencies ...
Ling Chen +6 more
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Graph Neural Network for Traffic Forecasting: The Research Progress
ISPRS Int. J. Geo Inf., 2023 Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the
Weiwei Jiang +3 more
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DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs [PDF]
Workshop on Irregular Applications: Architectures and Algorithms, 2020 Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search.
Da Zheng +8 more
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Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]
, 2001 Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán +6 more
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Framework and Algorithms for Accelerating Training of Semi-supervised Graph Neural Network Based on Heuristic Coarsening Algorithms [PDF]
Jisuanji kexueGraph neural network is the mainstream tool of graph machine learning at the current stage,and it has broad development prospects.By constructing an abstract graph structure,the graph neural network model can be used to efficiently deal with problems in ...
CHEN Yufeng , HUANG Zengfeng
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