With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work.

pdf/spatio-temporal-graph-neural-networks-for-predictive-2lpp1g78.pdf

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy. Intra-series irregularity refers to the fact that time-series signals are often recorded at irregular intervals, while inter-series discrepancy refers to the significant variability in sampling rates among diverse series. However, recent advances in irregular time series have primarily focused on addressing intra-series irregularity, overlooking the issue of inter-series discrepancy. To bridge this gap, we present Warpformer, a novel approach that fully considers these two characteristics. In a nutshell, Warpformer has several crucial designs, including a specific input representation that explicitly characterizes both intra-series irregularity and inter-series discrepancy, a warping module that adaptively unifies irregular time series in a given scale, and a customized attention module for representation learning. Additionally, we stack multiple warping and attention modules to learn at different scales, producing multi-scale representations that balance coarse-grained and fine-grained signals for downstream tasks. We conduct extensive experiments on widely used datasets and a new large-scale benchmark built from clinical databases. The results demonstrate the superiority of Warpformer over existing state-of-the-art approaches.

/pdf/warpformer-a-multi-scale-modeling-approach-for-irregular-2m149trx.pdf

Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmission mechanisms of infectious diseases. However, they often fall short when confronted with the growing challenges of today. Consequently, Graph Neural Networks (GNNs) have emerged as a progressively popular tool in epidemic research. In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions. To accomplish this objective, we introduce hierarchical taxonomies for both epidemic tasks and methodologies, offering a trajectory of development within this domain. For epidemic tasks, we establish a taxonomy akin to those typically employed within the epidemic domain. For methodology, we categorize existing work into \textit{Neural Models} and \textit{Hybrid Models}. Following this, we perform an exhaustive and systematic examination of the methodologies, encompassing both the tasks and their technical details. Furthermore, we discuss the limitations of existing methods from diverse perspectives and systematically propose future research directions. This survey aims to bridge literature gaps and promote the progression of this promising field. We hope that it will facilitate synergies between the communities of GNNs and epidemiology, and contribute to their collective progress.

A Review of Graph Neural Networks in Epidemic Modeling

Traffic flow imputation provides a more-complete view of traffic flows, and thus is a fundamental function in building Intelligent Transportation Systems. The performance of traffic flow imputation has a big impact on a wide range of downstream applications, such as traffic forecasting and control. Therefore, in this paper, we propose a Multi-grAph Convolutional Recurrent netwOrk (MACRO) framework for supporting fine-grained lane-level traffic flow imputation, which can help to reconstruct more complete traffic flows at the lane level. Specifically, we first design a spatial dependency module to model the diversified spatial correlations within traffic flows, where multi-relation graphs are first constructed to consider correlations from various perspective, then a multi-graph convolution neural network is proposed to capture the integrated spatial dependencies of traffic flows and adequately propagate the observed traffic values to mitigate data sparsity problem from spatial domain. Also, to handle the temporally continuous data missing issue, we adopt a modified bi-directional recurrent neural network to capture traffic flows’ temporal dependencies by considering both historical and future information, and employ a temporal decay mechanism to control the irregular information transfer between adjacent time slices. Moreover, a spatio-temporal knowledge integration module is devised to comprehensively integrate multi-resolution spatiotemporal knowledge for traffic flow imputation. Finally, extensive experiments on the real-world dataset demonstrate that the performance of MACRO outperforms several state-of-the-art baselines with respect to traffic flow imputation.

Multi-Graph Convolutional Recurrent Network for Fine-Grained Lane-Level Traffic Flow Imputation

Traffic flow imputation provides a more-complete view of traffic flows, and thus is a fundamental function in building Intelligent Transportation Systems. The performance of traffic flow imputation has a big impact on a wide range of downstream applications, such as traffic forecasting and control. Therefore, in this paper, we propose a Multi-grAph Convolutional Recurrent netwOrk (MACRO) framework for supporting fine-grained lane-level traffic flow imputation, which can help to reconstruct more complete traffic flows at the lane level. Specifically, we first design a spatial dependency module to model the diversified spatial correlations within traffic flows, where multi-relation graphs are first constructed to consider correlations from various perspective, then a multi-graph convolution neural network is proposed to capture the integrated spatial dependencies of traffic flows and adequately propagate the observed traffic values to mitigate data sparsity problem from spatial domain. Also, to handle the temporally continuous data missing issue, we adopt a modified bi-directional recurrent neural network to capture traffic flows’ temporal dependencies by considering both historical and future information, and employ a temporal decay mechanism to control the irregular information transfer between adjacent time slices. Moreover, a spatio-temporal knowledge integration module is devised to comprehensively integrate multi-resolution spatiotemporal knowledge for traffic flow imputation. Finally, extensive experiments on the real-world dataset demonstrate that the performance of MACRO outperforms several state-of-the-art baselines with respect to traffic flow imputation. 

Talent demand and supply forecasting aims to model the variation of the labor market, which is crucial to companies for recruitment strategy adjustment and to job seekers for proactive career path planning. However, existing approaches either focus on talent demand or supply forecasting, but overlook the interconnection between demand-supply sequences among different companies and positions. To this end, in this paper, we propose a Dynamic Heterogeneous Graph Enhanced Meta-learning (DH-GEM) framework for fine-grained talent demand-supply joint prediction. Specifically, we first propose a Demand-Supply Joint Encoder-Decoder (DSJED) and a Dynamic Company-Position Heterogeneous Graph Convolutional Network (DyCP-HGCN) to respectively capture the intrinsic correlation between demand and supply sequences and company-position pairs. Moreover, a Loss-Driven Sampling based Meta-learner (LDSM) is proposed to optimize long-tail forecasting tasks with a few training data. Extensive experiments have been conducted on three real-world datasets to demonstrate the effectiveness of our approach compared with five baselines. DH-GEM has been deployed as a core component of the intelligent human resource system of a cooperative partner.

Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning

The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market. Existing efforts in this area either rely on domain-expert knowledge or regarding skill evolution as a simplified time series forecasting problem. However, both approaches overlook the sophisticated relationships among different skills and the inner-connection between skill demand and supply variations. In this paper, we propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, and iii) a conditional hyper-decoder to jointly predict demand and supply variations by incorporating historical demand-supply gaps. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed framework compared to seven baselines and the effectiveness of the three modules.

pdf/a-cross-view-hierarchical-graph-learning-hypernetwork-for-l9nrldnytf.pdf

A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction

Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modelled human mobility via macro indicators (e.g., average daily travel distance) and then study the effectiveness of NPIs. In this work, we focus on mobility modelling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information).
To address these challenges and jointly model variables including a geometric graph, a set of diffusions and a set of locations, we propose a model named Deep Graph Diffusion Infomax (DGDI). We show the maximization of DGDI can be bounded by two tractable components: a univariate Mutual Information (MI) between geometric graph and diffusion representation, and a univariate MI between diffusion representation and location representation. To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods.

pdf/human-mobility-modeling-during-the-covid-19-pandemic-via-319sa4qv.pdf

Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax

Labor Migration Modeling Through Large-Scale Job Query Data

During the creation of graphic designs, individuals inevitably spend a lot of time and effort on adjusting visual attributes (e.g., positions, colors, and fonts) of elements to make them more aesthetically pleasing. It is a trial-and-error process, requires repetitive edits, and relies on good design knowledge. In this work, we seek to alleviate such difficulty by automatically suggesting aesthetic improvements, i.e., taking an existing design as the input and generating a refined version with improved aesthetic quality as the output. This goal presents two challenges: proposing a refined design based on the user-given one, and assessing whether the new design is better aesthetically. To cope with these challenges, we propose a design principle-guided candidate generation stage and a data-driven candidate evaluation stage. In the candidate generation stage, we generate candidate designs by leveraging design principles as the guidance to make changes around the existing design. In the candidate evaluation stage, we learn a ranking model upon a dataset that can reflect humans aesthetic preference, and use it to choose the most aesthetically pleasing one from the generated candidates. We implement a prototype system on presentation slides and demonstrate the effectiveness of our approach through quantitative analysis, sample results, and user studies.

Aesthetics++: Refining Graphic Designs by Exploring Design Principles and Human Preference

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