Spatio-Temporal Dual Graph Attention Network for Query-POI Matching

Yuan, Zixuan; Líu, Hao; Liu, Yanchi; Zhang, Denghui; Yi, Fei; Zhu, Nengjun; Xiong, Hui

doi:10.1145/3397271.3401159

Cited by 50 publications

(13 citation statements)

References 22 publications

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“…More work along this line can be referred to [18,28]. Similarly, incorporating domain knowledge has shown effectiveness in broader areas [14,33,[38][39][40][41] such as representation learning. The above solutions have primarily leveraged domain corpus for pre-training in a straightforward way, without considering insightful domain characteristics and domain knowledge such as domain phrase and entity association.…”

Section: Related Workmentioning

confidence: 99%

Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment

Zhang

Yuan

Liu

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Motivated by the success of pre-trained language models such as BERT in a broad range of natural language processing (NLP) tasks, recent research efforts have been made for adapting these models for different application domains. Along this line, existing domainoriented models have primarily followed the vanilla BERT architecture and have a straightforward use of the domain corpus. However, domain-oriented tasks usually require accurate understanding of domain phrases, and such fine-grained phrase-level knowledge is hard to be captured by existing pre-training scheme. Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge. But meanwhile, by doing so there is a risk of introducing noise due to the lack of groundtruth word-level alignment. To address the above issues, we provide a generalized domain-oriented approach, which leverages auxiliary domain knowledge to improve the existing pre-training framework from two aspects. First, to preserve phrase knowledge effectively, we build a domain phrase pool as auxiliary training tool, meanwhile we introduce Adaptive Hybrid Masked Model to incorporate such knowledge. It integrates two learning modes, word learning and phrase learning, and allows them to switch between each other. Second, we introduce Cross Entity Alignment to leverage entity association as weak supervision to augment the semantic learning of pre-trained models. To alleviate the potential noise in this process, we introduce an interpretable Optimal Transport based approach to guide alignment learning. Experiments on four domain-oriented tasks demonstrate the superiority of our framework. CCS CONCEPTS• Computing methodologies → Natural language processing.

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Section: Related Workmentioning

confidence: 99%

Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment

Zhang

Yuan

Liu

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

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“…Specifically, in the context of PoI recommendation, Rahmani et al [20] proposed CATAPE, a method that simultaneously incorporates sequences of user locations and categories of PoI's visited by them to recommend potential new points of interest. Likewise, Yuan et al combined information from historical and contrasting users' queries to find mobility similarity [41]. Such approach focuses on recommending a PoI to a user through keywords in an incomplete query.…”

Section: Related Workmentioning

confidence: 99%

Understanding mobility in networks: A node embedding approach

Barros¹,

Ferreira²,

Santos³

et al. 2021

Preprint

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Motivated by the growing number of mobile devices capable of connecting and exchanging messages, we propose a methodology aiming to model and analyze node mobility in networks. We note that many existing solutions in the literature rely on topological measurements calculated directly on the graph of node contacts, aiming to capture the notion of the node's importance in terms of connectivity and mobility patterns beneficial for prototyping, design, and deployment of mobile networks. However, each measure has its specificity and fails to generalize the node importance notions that ultimately change over time. Unlike previous approaches, our methodology is based on a node embedding method that models and unveils the nodes' importance in mobility and connectivity patterns while preserving their spatial and temporal characteristics. We focus on a case study based on a trace of group meetings. The results show that our methodology provides a rich representation for extracting different mobility and connectivity patterns, which can be helpful for various applications and services in mobile networks.

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“…Recently, lots of researchers have studies dual-attention graph neural networks [ 37 , 38 ] and developed a serious of application for general spatio-temporal network in different urban traffic scene [ 39 , 40 ].…”

Section: Related Workmentioning

confidence: 99%

SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

Mei

Liu

Pan

et al. 2020

Sensors

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There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task.

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Spatio-Temporal Dual Graph Attention Network for Query-POI Matching

Cited by 50 publications

References 22 publications

Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment

Domain-oriented Language Modeling with Adaptive Hybrid Masking and Optimal Transport Alignment

Understanding mobility in networks: A node embedding approach

SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

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