Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction

Wang, Chunyang; Zhu, Yanmin; Zang, Tianzi; Liu, Haobing; Yu, Jiadi

doi:10.1145/3437963.3441731

Cited by 51 publications

(26 citation statements)

References 20 publications

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“…Hourly scaled dataset of pollutants (𝑃𝑀2.5, 𝑃𝑀10, 𝑁𝑂2, 𝑆𝑂2, 𝑂3, 𝐶𝑂) from 76 station [83,217,271] [url]…”

Section: Graph/network Structured Datamentioning

confidence: 99%

“…The recent advancements in GNN better address these existing problems in the ESE field. Air quality, there are many state-ofart works aim to predict the PM2.5/air quality using ST-GNN (GNN + RNN) [79,83,170,208,217,232,271,272,291], the rationale for using this model structure is because the air quality problem is naturally related to the regions (spatial-GNN) and changes over time (temporal-RNN). Water state, in water state prediction and management field, most works also follow the same graph (regions as nodes) structure and focusing on problems like water flow prediction [109], water quality prediction [292], large sample hydrology problem [251], and water network partitioning problem [224], where [224,292] only consider GNN rather than ST-GNN.…”

Section: Health Inference and Informaticsmentioning

confidence: 99%

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Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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“…Hourly scaled dataset of pollutants (𝑃𝑀2.5, 𝑃𝑀10, 𝑁𝑂2, 𝑆𝑂2, 𝑂3, 𝐶𝑂) from 76 station [83,217,271] [url]…”

Section: Graph/network Structured Datamentioning

confidence: 99%

Section: Health Inference and Informaticsmentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides, we use the Adam [15] optimizer with a learning rate of 0.001, and the batch size is set as 512. In InfNet, we search the hidden sizes 𝑐 in the range [16,32,64,128], and 𝑐 = 64 reaches the best performance. For the GNNs in Eq.…”

Section: Experiments 51 Experimental Setupmentioning

confidence: 99%

“…Deep learning on graphs. Recently, GNNs have achieved stateof-the-art performance on many tasks [16,31,32,40]. Based on the Message-Passing framework, many works have expanded the capabilities to tackle different types of graph, such as heterogeneous graphs [14] and dynamic graphs [13,45].…”

Section: Related Workmentioning

confidence: 99%

How Powerful are Interest Diffusion on Purchasing Prediction

Huang

Yang

Cheng

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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A Taocode is a kind of specially coded text-link on Taobao.com (the world's biggest online shopping website), through which users can share messages about products with each other. Analyzing Taocodes can potentially facilitate understanding of the social relationships between users and, more excitingly, their online purchasing behaviors under the influence of Taocode diffusion. This paper innovatively investigates the problem of online purchasing predictions from an information diffusion perspective, with Taocode as a case study. Specifically, we conduct profound observational studies on a large-scale real-world dataset from Taobao, containing over 100M Taocode sharing records. Inspired by our observations, we propose InfNet, a dynamic GNN-based framework that models the information diffusion across Taocode. We then apply InfNet to item purchasing predictions. Extensive experiments on real-world datasets validate the effectiveness of InfNet compared with 8 stateof-the-art baselines.

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“…The GNLMS algorithm in [9] is used to conduct online estimation of temperature recordings from weather stations; it would more beneficial if other data such as air quality, wind speed, precipitation, and humidity could be estimated simultaneously with the temperature. In [26], air pollutants recordings in weather stations, including CO, NO2, O3, PM10, PM2.5, and SO2, are being monitored using a fusion of the Attention mechanism and the GCN, but the inherent bulkiness of Neural Networks caused by their high complexity prohibits them to be applied to lowcost applications. Thus, there is a need for online processing of graph signals with multiple features defined on a single graph topology at a low cost.…”

Section: Introductionmentioning

confidence: 99%

Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

Yan,

Adel,

Kuruoglu

2022

Preprint

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In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise. Different from leastsquares-based algorithms, such as the adaptive GSP Least Mean Squares (GLMS) algorithm and the normalized GLMS (GNLMS) algorithm, the GNLMP algorithm has the ability to reconstruct a graph signal that is corrupted by non-Gaussian noise with heavy-tailed characteristics. Compared to the recently introduced adaptive GSP least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number of iterations to converge to a steady graph signal. The convergence condition of the GNLMP algorithm is derived, and the ability of the GNLMP algorithm to process multidimensional time-varying graph signals with multiple features is demonstrated as well. Simulations show the performance of the GNLMP algorithm in estimating steady-state and time-varying graph signals is faster than GLMP and more robust in comparison to GLMS and GNLMS.

show abstract

Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction

Cited by 51 publications

References 20 publications

Graph Neural Networks in IoT: A Survey

Graph Neural Networks in IoT: A Survey

How Powerful are Interest Diffusion on Purchasing Prediction

Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise

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