Search Results - "graph neural network"

Modern dynamic systems, such as transportation networks and IoT infrastructures, generate massive volumes of interrelated temporal data represented as temporal graphs. Conventional methods – like clustering, statistical thresholds, and classical time series analysis – often fail to account for the s...

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Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and c...

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Abstract Precise news recommendations are critical in today’s digital landscape. However, conventional approaches overlook fine-grained sentiment nuances associated with individual entities in news content. This paper presents SentiEntityRec, a novel graph neural network framework that enriches trad...

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Traffic forecasting is the task of predicting future traffic based on historical traffic data. It is challenging due to the complex spatial-temporal correlation on road networks. Most existing research works use sequential Graph Neural Networks (GNN) to model traffic inference. However, they only fo...

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In the field of electrical power material management, it is paramount that users receive accurate recommendations regarding the electrical power materials they require. Recently, a growing number of studies have been dedicated to graph neural network (GNN)-based recommendation systems due to their a...

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Recent advancements in connected vehicle technology have the potential to transform the efficiency and sustainability of transportation systems. Among these, truck platooning has emerged as a promising strategy to enhance freight delivery operations. However, the generalization of truck platoon mode...

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The global prevalence of depression necessitates the application of technological solutions, particularly sensor-based systems, to augment scarce resources for early diagnostic purposes. In this study, we use benchmark datasets that contain multimodal data including video, audio, and transcribed tex...

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Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is d...

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Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process. By harnessing the power of machine learning-based methods, CAD designers can...

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To improve learning on irregular 3D shapes, such as meshes with varying discretizations and point clouds with different samplings, we propose L2-GNN, a new graph neural network that approximates the spectral filters using twice linear parameterization. First, we parameterize the spectral filters usi...

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Neural operators have emerged as a powerful tool for learning mappings between function spaces, particularly for solving partial differential equations (PDEs). This study introduces a novel framework that unifies Graph Neural Networks (GNNs) and Transformers, combining their complementary strengths...

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The automated identification and evaluation of potential attack paths within infrastructures is a critical aspect of cybersecurity risk assessment. However, existing methods become impractical when applied to complex infrastructures. While machine learning (ML) has proven effective in predicting the...

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In distribution systems, limited measurement configurations and communication constraints often result in a low success rate of data acquisition, posing challenges to both system observability and the real-time performance required by state estimation (SE). Consequently, the distribution system SE (...

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Polypharmacy, the concurrent use of multiple drugs, is a common approach to treating patients with complex diseases or multiple conditions. Although consuming a combination of drugs can be beneficial in some cases, it can lead to unintended drug-drug interactions (DDI) and increase the risk of adver...

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Microservice is a popular architecture for developing cloud-native applications. However, decomposing a monolithic application into microservices remains a challenging task. This complexity arises from the need to account for factors such as component dependencies, cohesive clusters, and bounded con...

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Traditional systems often focus on a singular action type, such as a purchase, while overlooking the rich behavioral pluralism that is inherent in user engagement, such as the transient clicks or latent purchase intents signaled by cart additions. This narrow focus leads to sparse datasets and hinde...

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This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics&g...

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Ensuring robust and efficient service restoration in Wavelength Division Multiplexing (WDM) networks is crucial for maintaining network reliability amidst failures caused by disasters, equipment malfunctions, or power outages. This article presents a hybrid framework that integrates Deep Reinforceme...

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Multi-Version Concurrency Control (MVCC) is a critical mechanism in blockchain systems such as Hyperledger Fabric. MVCC is crucial for managing concurrent transactions ensuring integrity through versioning and timestamp techniques. Enhancing MVCC is essential due to the significant overhead involved...

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Deep neural networks demonstrate high accuracy in detecting harmful social media posts; however, conventional text-based methods often overlook critical contextual relationships among posts, users, and shared information. Graph-based methods, particularly those employing graph neural networks, effec...

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