Urban Region Pre-training and Prompting: A Graph-based Approach

Ridge regression: Biased estimation for nonorthogonal problems

Arthur E Hoerl and Robert W Kennard. 1970 · 1970

Earlier work this paper cites.

A computer movie simulating urban growth in the Detroit region

Waldo R Tobler. 1970 · 1970

Earlier work this paper cites.

Unsupervised representation learning of spatial data via multimodal embedding. In Proceedings of the 28th ACM international conference on information and knowledge management (CIKM) . 1993–2002

Porter Jenkins, Ahmad Farag, Suhang Wang, and Zhenhui Li. 2019 · 2002

Earlier work this paper cites.

Graph kernels

S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt. 2010 · 2010

Earlier work this paper cites.

Deep metric learning using triplet network. In Proceedings of Similarity-based pattern recognition: third international workshop (SIMBAD) . Springer, 84–92

Elad Hoffer and Nir Ailon. 2015 · 2015

Earlier work this paper cites.

Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI conference on Artificial Intelligence (AAAI) , Vol. 29. 2181–2187

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015

Earlier work this paper cites.

ODIAC fossil fuel CO2 emissions dataset (Version name: ODIAC2023)

Tomohiro Oda and Shamil Maksyutov. 2015 · 2015

Earlier work this paper cites.

node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) . 855–864

Aditya Grover and Jure Leskovec. 2016 · 2016

Earlier work this paper cites.

Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 770–778

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016

Earlier work this paper cites.

Variational graph auto-encoders

Original

Thomas N Kipf and Max Welling. 2016 · 2016

Earlier work this paper cites.

metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) . 135–144

Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017 · 2017

Earlier work this paper cites.

Region representation learning via mobility flow. In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM) . 237–246

Hongjian Wang and Zhenhui Li. 2017 · 2017

Earlier work this paper cites.

Representing urban functions through zone embedding with human mobility patterns. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI) . 3919–3925

Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, and Hui Xiong. 2018 · 2018

Earlier work this paper cites.

Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction. In Proceedings of the 28th ACM international conference on information and knowledge management (CIKM) . 2293–2296

Lei Bai, Lina Yao, Salil S Kanhere, Xianzhi Wang, Wei Liu, and Zheng Yang. 2019 · 2019

Earlier work this paper cites.

Finding attribute-aware similar region for data analysis

Kaiyu Feng, Gao Cong, Christian S Jensen, and Tao Guo. 2019 · 2019

Earlier work this paper cites.

Efficient region embedding with multi-view spatial networks: A perspective of locality-constrained spatial autocorrelations. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , Vol. 33. 906–913

Yanjie Fu, Pengyang Wang, Jiadi Du, Le Wu, and Xiaolin Li. 2019 · 2019

Earlier work this paper cites.

Contextualized spatial–temporal network for taxi origin-destination demand prediction

Lingbo Liu, Zhilin Qiu, Guanbin Li, Qing Wang, Wanli Ouyang, and Liang Lin. 2019 · 2019

Earlier work this paper cites.

Heterogeneous graph transformer. In Proceedings of the Web conference 2020 (WWW) . 2704–2710

Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020 · 2020

Earlier work this paper cites.

Graph meta learning via local subgraphs. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS) , Vol. 33. 5862–5874

Kexin Huang and Marinka Zitnik. 2020 · 2020

Earlier work this paper cites.

How Much Knowledge Can You Pack Into the Parameters of a Language Model?. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 5418–5426

Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020

Earlier work this paper cites.

Multi-view joint graph representation learning for urban region embedding. In Proceedings of the 29th International Joint Conferences on Artificial Intelligence (IJCAI) . 4431–4437

Mingyang Zhang, Tong Li, Yong Li, and Pan Hui. 2021 · 2021

Earlier work this paper cites.

Discovering association rules from big graphs

Wenfei Fan, Wenzhi Fu, Ruochun Jin, Ping Lu, and Chao Tian. 2022a · 2022

Earlier work this paper cites.

Parallel rule discovery from large datasets by sampling. In Proceedings of the 2022 International Conference on Management of Data (SIGMOD) . 384–398

Wenfei Fan, Ziyan Han, Yaoshu Wang, and Min Xie. 2022b · 2022

Cited alongside, same era.

Kergnns: Interpretable graph neural networks with graph kernels. In Proceedings of the AAAI conference on artificial intelligence (AAAI) , Vol. 36. 6614–6622

Aosong Feng, Chenyu You, Shiqiang Wang, and Leandros Tassiulas. 2022 · 2022

Cited alongside, same era.

Multi-graph fusion networks for urban region embedding. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI) . 2312–2318

Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, and Cheng Wang. 2022 · 2022

Cited alongside, same era.

Classifying urban functional regions by integrating buildings and points-of-interest using a stacking ensemble method

Min Yang, Bo Kong, Ruirong Dang, and Xiongfeng Yan. 2022 · 2022

Cited alongside, same era.

Learning to prompt for vision-language models

What can Foursquare do for you?

Foursquare 2024 · 2024

Closest in time.

Learning context-aware region similarity with effective spatial normalization over Point-of-Interest data

Jiahui Jin, Yifan Song, Dong Kan, Binjie Zhang, Yan Lyu, Jinghui Zhang, and Hongru Lu. 2024 · 2024

Closest in time.

Can we soft prompt LLMs for graph learning tasks?. In Proceedings of the ACM Web Conference 2024 (WWW) . 481–484

Zheyuan Liu, Xiaoxin He, Yijun Tian, and Nitesh V Chawla. 2024 · 2024

Closest in time.

Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks. In Proceedings of the ACM Web Conference 2024 (WWW) . 1015–1023

Yihong Ma, Ning Yan, Jiayu Li, Masood Mortazavi, and Nitesh V Chawla. 2024 · 2024

Closest in time.

UUKG: unified urban knowledge graph dataset for urban spatiotemporal prediction. In Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS) , Vol. 36. 62442–62456

Yansong Ning, Hao Liu, Hao Wang, Zhenyu Zeng, and Hui Xiong. 2024 · 2024