Fetching the paper…
Reading the bibliography…
In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks.
A. Pinkus, “Weierstrass and approximation theory,” Journal of Approximation Theory , vol. 107, no. 1, pp. 1–66, 2000
2000
Earlier work this paper cites.
A. Rahimi and B. Recht, “Random features for large-scale kernel machines,” in NIPS , 2007, pp. 1177–1184
2007
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in NIPS , 2013, pp. 3111–3119
2013
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: online learning of social representations,” in KDD , 2014, pp. 701–710
2014
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in KDD , 2016, pp. 855–864
2016
Earlier work this paper cites.
H. Wu, Z. Chen, W. Sun, B. Zheng, and W. Wang, “Modeling trajectories with recurrent neural networks,” in IJCAI , C. Sierra, Ed., 2017, pp. 3083–3090
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
L. F. R. Ribeiro, P. H. P. Saverese, and D. R. Figueiredo, “ struc2vec : Learning node representations from structural identity,” in KDD , 2017, pp. 385–394
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Earlier work this paper cites.
Q. Li, Z. Han, and X. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in AAAI , 2018, pp. 3538–3545
2018
Earlier work this paper cites.
T. S. Jepsen, C. S. Jensen, T. D. Nielsen, and K. Torp, “On network embedding for machine learning on road networks: A case study on the danish road network,” in IEEE BigData 2018, , 2018, pp. 3422–3431
2018
Earlier work this paper cites.
C. Yang and G. Gidofalvi, “Fast map matching, an algorithm integrating hidden markov model with precomputation,” International Journal of Geographical Information Science , vol. 32, no. 3, pp. 547–570, 2018
2018
Earlier work this paper cites.
P. Shaw, J. Uszkoreit, and A. Vaswani, “Self-attention with relative position representations,” in NAACL-HLT , 2018, pp. 464–468
2018
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in ICLR , 2018
2018
Earlier work this paper cites.
J. Hu, C. Guo, B. Yang, and C. S. Jensen, “Stochastic weight completion for road networks using graph convolutional networks,” in ICDE , 2019, pp. 1274–1285
2019
Earlier work this paper cites.
M. Wang, W. Lee, T. Fu, and G. Yu, “Learning embeddings of intersections on road networks,” in SIGSPATIAL , 2019, pp. 309–318
2019
Earlier work this paper cites.
T. S. Jepsen, C. S. Jensen, and T. D. Nielsen, “Graph convolutional networks for road networks,” in SIGSPATIAL , 2019, pp. 460–463
2019
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL , 2019, pp. 4171–4186
2019
Cited alongside, same era.
J. Lu, D. Batra, D. Parikh, and S. Lee, “Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,” in NeurIPS , 2019, pp. 13–23
2019
Cited alongside, same era.
C. Sun, A. Myers, C. Vondrick, K. Murphy, and C. Schmid, “Videobert: A joint model for video and language representation learning,” in ICCV , 2019, pp. 7463–7472
2019
Cited alongside, same era.
X. Li, G. Cong, and Y. Cheng, “Spatial transition learning on road networks with deep probabilistic models,” in ICDE , 2020, pp. 349–360
2020
Cited alongside, same era.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , 2020
B. Hui, D. Yan, H. Chen, and W. Ku, “Trajnet: A trajectory-based deep learning model for traffic prediction,” in KDD , F. Zhu, B. C. Ooi, and C. Miao, Eds., 2021, pp. 716–724
2021
Later among the works it cites.
M. Li, P. Tong, M. Li, Z. Jin, J. Huang, and X. Hua, “Traffic flow prediction with vehicle trajectories,” in AAAI , 2021, pp. 294–302
2021
Later among the works it cites.
B. Wang, E. D. Buccio, and M. Melucci, “Word2fun: Modelling words as functions for diachronic word representation,” in NeurIPS 2021 , 2021, pp. 2861–2872
2021
Later among the works it cites.
J. Wang, N. Wu, and W. X. Zhao, “Personalized route recommendation with neural network enhanced search algorithm,” IEEE Trans. Knowl. Data Eng. , vol. 34, no. 12, pp. 5910–5924, 2022
2022
Later among the works it cites.
L. Du, X. Shi, Q. Fu, X. Ma, H. Liu, S. Han, and D. Zhang, “GBK-GNN: gated bi-kernel graph neural networks for modeling both homophily and heterophily,” in The ACM Web Conference 2022 , 2022, pp. 1550–1558
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
N. Wu, W. X. Zhao, J. Wang, and D. Pan, “Learning effective road network representation with hierarchical graph neural networks,” in KDD , 2020, pp. 6–14
2020
Cited alongside, same era.
T. Fu and W. Lee, “Trembr: Exploring road networks for trajectory representation learning,” ACM Trans. Intell. Syst. Technol. , vol. 11, no. 1, pp. 10:1–10:25, 2020
2020
Cited alongside, same era.
Y. Liu, K. Zhao, G. Cong, and Z. Bao, “Online anomalous trajectory detection with deep generative sequence modeling,” in ICDE , 2020, pp. 949–960
2020
Cited alongside, same era.
Z. Yang, H. Sun, J. Huang, Z. Sun, H. Xiong, S. Qiao, Z. Guan, and X. Jia, “An efficient destination prediction approach based on future trajectory prediction and transition matrix optimization,” IEEE Trans. Knowl. Data Eng. , vol. 32, no. 2, pp. 203–217, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A lite BERT for self-supervised learning of language representations,” in ICLR , 2020
2020
Cited alongside, same era.
S. Guo, Y. Lin, H. Wan, X. Li, and G. Cong, “Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
T. S. Jepsen, C. S. Jensen, and T. D. Nielsen, “Relational fusion networks: Graph convolutional networks for road networks,” IEEE Trans. Intell. Transp. Syst. , vol. 23, no. 1, pp. 418–429, 2022
2022
Later among the works it cites.
Z. Mao, Z. Li, D. Li, L. Bai, and R. Zhao, “Jointly contrastive representation learning on road network and trajectory,” in CIKM , 2022, pp. 1501–1510
2022
Later among the works it cites.
H. Yuan, G. Li, and Z. Bao, “Route travel time estimation on A road network revisited: Heterogeneity, proximity, periodicity and dynamicity,” Proc. VLDB Endow. , vol. 16, no. 3, pp. 393–405, 2022
2022
Later among the works it cites.
S. Wu, X. Yan, X. Fan, S. Pan, S. Zhu, C. Zheng, M. Cheng, and C. Wang, “Multi-graph fusion networks for urban region embedding,” in IJCAI , 2022, pp. 2312–2318
2022
Later among the works it cites.
Y. Liang, K. Ouyang, Y. Wang, X. Liu, H. Chen, J. Zhang, Y. Zheng, and R. Zimmermann, “Trajformer: Efficient trajectory classification with transformers,” in CIKM , 2022, pp. 1229–1237
2022
Later among the works it cites.
M. Gu, G. Yang, S. Zhou, N. Ma, J. Chen, Q. Tan, M. Liu, and J. Bu, “Homophily-enhanced structure learning for graph clustering,” in CIKM , 2023, pp. 577–586
2023
Later among the works it cites.
Y. Chang, E. Tanin, X. Cao, and J. Qi, “Spatial structure-aware road network embedding via graph contrastive learning,” in EDBT , 2023, pp. 144–156
2023
Later among the works it cites.
L. Zhang and C. Long, “Road network representation learning: A dual graph-based approach,” ACM Trans. Knowl. Discov. Data , vol. 17, no. 9, pp. 121:1–121:25, 2023
2023
Later among the works it cites.
S. Schestakov, P. Heinemeyer, and E. Demidova, “Road network representation learning with vehicle trajectories,” in PAKDD , 2023, pp. 57–69
2023
Later among the works it cites.
L. Zhang, C. Long, and G. Cong, “Region embedding with intra and inter-view contrastive learning,” IEEE Trans. Knowl. Data Eng. , vol. 35, no. 9, pp. 9031–9036, 2023
2023
Later among the works it cites.
J. Jiang, D. Pan, H. Ren, X. Jiang, C. Li, and J. Wang, “Self-supervised trajectory representation learning with temporal regularities and travel semantics,” in ICDE , 2023, pp. 843–855
2023
Later among the works it cites.