Traffic Flow Prediction via Spatial Temporal Graph Neural Network

Wang, Xiaoyang; Ma, Yao; Wang, Yiqi; Jin, Wei; Wang, Xin; Tang, Jiliang; Jia, Caiyan; Yu, Jian

doi:10.1145/3366423.3380186

Cited by 378 publications

(145 citation statements)

References 27 publications

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“…Over last few years, Graph Convolutional Networks (GCNs) have benefited many real world applications across different domains, such as molecule design [37], financial fraud detection [29], traffic prediction [30,38], and user behavior analysis [11,18,27]. One of the most important and challenging applications for GCNs is to classify nodes in a semi-supervised manner.…”

Section: Introductionmentioning

confidence: 99%

Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks

Tang

Yao

Sun

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkable success of GCNs, it is difficult to train GCNs with insufficient supervision. When labeled data are limited, the performance of GCNs becomes unsatisfying for low-degree nodes. While some prior work analyze successes and failures of GCNs on the entire model level, profiling GCNs on individual node level is still underexplored. In this paper, we analyze GCNs in regard to the node degree distribution. From empirical observation to theoretical proof, we confirm that GCNs are biased towards nodes with larger degrees with higher accuracy on them, even if high-degree nodes are underrepresented in most graphs. We further develop a novel Self-Supervised-Learning Degree-Specific GCN (SL-DSGCN) that mitigate the degree-related biases of GCNs from model and data aspects. Firstly, we propose a degree-specific GCN layer that captures both discrepancies and similarities of nodes with different degrees, which reduces the inner model-aspect biases of GCNs caused by sharing the same parameters with all nodes. Secondly, we design a self-supervised-learning algorithm that creates pseudo labels with uncertainty scores on unlabeled nodes with a Bayesian neural network. Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective. Uncertainty scores are further exploited to weight pseudo labels dynamically in the stochastic gradient descent for SL-DSGCN. Experiments on three benchmark datasets show SL-DSGCN not only outperforms state-of-the-art selftraining/self-supervised-learning GCN methods, but also improves GCN accuracy dramatically for low-degree nodes.

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Section: Introductionmentioning

confidence: 99%

Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks

Tang

Yao

Sun

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…Guo et al [161] propose two parts of modules to encode historical data: The first part leverages the attention mechanism to capture the dynamic spatio-temporal correlations in traffic data, while the second part uses the GCN technique to capture the spatial patterns and common standard convolutions to describe the temporal features. Also, some people [162,163] consider the sequential features among historical network flows, so they further append RNN models to encode historical data. Specifically, Li et al [162] model the traffic flow as a diffusion process on a directed graph and introduce diffusion convolutional recurrent neural network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependencies in the traffic flow.…”

Section: Traffic Forecastingmentioning

confidence: 99%

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

191

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Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

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“…Lu and Li [282] used graph attention network on user network for fake news detection. Zhong et al [283] applied graph convolutional network on reply relationship network for controversy detection. Sachan et al [113] employed a topic model to compute the community distribution of each user in a social network.…”

Section: A22 Embedding-based Representationmentioning

confidence: 99%

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

et al. 2021

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Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS?To explore the answer, we give a thorough review of data representations in CSS for both text and network.Specifically, we summarize existing representations into two schemes, namely symbol-based and embeddingbased representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.

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Traffic Flow Prediction via Spatial Temporal Graph Neural Network

Cited by 378 publications

References 27 publications

Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks

Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

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