Search Results - "graph neural network"

Sequential personalized recommendations, such as next best offer prediction or modeling demand evolution for next basket prediction, remain a key challenge for businesses. In recent years, deep learning models have been applied to solve these problems and demonstrated high feasibility. With the int...

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Accurately and quickly predicting the crystal structures of unknown materials can significantly accelerate the discovery of new materials. However, stable crystal structures only exist at the global minimum of formation energy, making optimization based on density functional theory calculations or g...

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The use of machine learning (ML) tools to address the challenges posed in next generation of wireless communication systems has been gaining significant momentum. In this paper, we investigate the use of self-supervised data-driven schemes for precoder optimization in the downlink of a Multiple-Inpu...

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Substitutional doping of fullerenes represents a significant category of heterofullerenes. Due to the vast number of isomers, confirming the ground state structure poses considerable challenges. In this study, we generated isomers of C_{60−<i>n</i>}B<sub><i&g...

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Abstract Heterogeneous graphs generally refer to graphs with different types of nodes and edges. A common approach for extracting useful information from heterogeneous graphs is to use meta‐graphs, which can be seen as a special kind of directed acyclic graph with same node and edge types as the het...

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With the evolution of green communication networks, device-level energy saving approaches face diminishing returns due to fundamental hardware limitations, while persistent traffic imbalances in metropolitan transport networks create untapped optimization potential. This work reveals that the inhere...

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The precise forecasting of mesoscale eddy trajectories holds significant importance for understanding their mechanisms in driving global oceanic mass and heat transport. However, mesoscale eddies are influenced by numerous stochastic and uncertain factors, leading to substantial fluctuations in thei...

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Code clone detection underpins a wide range of maintenance tasks from automated refactoring to real time plagiarism policing yet single view methods that rely on raw tokens, Abstract Syntax Trees or Control Flow Graphs still struggle to type-4 (semantic) clones. We present DG Clone, a dual graph se...

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Accurate spatiotemporal wind energy forecasting is essential for ensuring grid stability and maximizing the efficiency of renewable energy systems. This paper addresses the challenge of modeling the complex spatial and temporal dependencies inherent in wind power generation by presenting a comprehen...

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Fatigue detection remains a critical research focus in the field. Recent studies have attempted to enhance detection performance through multimodal information fusion, yet they largely overlook the impact of functional connectivity among multimodal signals. To address this limitation, we propose a n...

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Collaborative geometric reconstruction of building structures can significantly reduce communication consumption for data sharing, protect privacy, and provide support for large-scale robot application management. In recent years, geometric reconstruction of building structures has been partially st...

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Cyberbullying is one of the harmful activities on social networks that particularly affects the mental well-being of adolescents. Recent research has focused on session-based approaches to cyberbullying detection, which consider various components of a social media session, including posts, comments...

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The generalization of nautical charts remains crucial in geographic information science and cartography. Traditional geometry-based methods have contributed to the advancement of automated generalization to a certain extent, but they still exhibit significant limitations in handling complex marine s...

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Aiming at the problems of traditional encrypted traffic classification methods limited by the imbalance of dataset classes and the unreliability of the features used in complex network environments, an encrypted traffic classification method based on parallel traffic graph and graph neural network w...

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This study proposes a novel hybrid model for stock volatility forecasting by integrating directional and temporal dependencies among financial time series and market regime changes into a unified modeling framework. Specifically, we design a novel Hurst Exponent Effective Transfer Entropy Graph Neur...

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Background/Objectives: Electroencephalogram (EEG) signals, which reflect brain activity, are widely used in emotion recognition. However, the variety of EEG features presents significant challenges in identifying key features, reducing redundancy, and simplifying the computational process. Methods:...

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As cyberattacks continue to rise alongside the rapid expansion of digital systems, effective threat detection remains a critical yet challenging task. While several machine learning approaches have been proposed, the use of graph neural networks (GNNs) for cyberattack detection has not yet been syst...

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Atomistic simulations of properties of materials at finite temperatures are computationally demanding and require models that are more efficient than the ab initio approaches. Machine learning (ML) and artificial intelligence (AI) address this issue by enabling accurate models with close to ab initi...

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Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We introduce PACKETCLIP which is a multi-modal framework combining packet data with natural language semantics through contrastive pre-training and hierarchical Graph Neural Network (GNN) reasoning...

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The global burden of multimorbidity is increasing yet poorly understood, owing to insufficient methods for modelling complex systems of conditions. In particular, hepatosplenic multimorbidity has been inadequately investigated. From 17 January to 16 February 2023, we examined 3186 individuals aged 5...

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