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Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning

Published: 25 July 2019 Publication History

Abstract

Unsupervised spatial representation learning aims to automatically identify effective features of geographic entities (i.e., regions) from unlabeled yet structural geographical data. Existing network embedding methods can partially address the problem by: (1) regarding a region as a node in order to reformulate the problem into node embedding; (2) regarding a region as a graph in order to reformulate the problem into graph embedding. However, these studies can be improved by preserving (1) intra-region geographic structures, which are represented by multiple spatial graphs, leading to a reformulation of collective learning from relational graphs; (2) inter-region spatial autocorrelations, which are represented by pairwise graph regularization, leading to a reformulation of adversarial learning. Moreover, field data in real systems are usually lack of labels, an unsupervised fashion helps practical deployments. Along these lines, we develop an unsupervised Collective Graph-regularized dual-Adversarial Learning (CGAL) framework for multi-view graph representation learning and also a Graph-regularized dual-Adversarial Learning (GAL) framework for single-view graph representation learning. Finally, our experimental results demonstrate the enhanced effectiveness of our method.

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  • (2024)Generative Adversarial Networks: Applications, Challenges, and Open IssuesDeep Learning - Recent Findings and Research10.5772/intechopen.113098Online publication date: 29-May-2024
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    cover image ACM Conferences
    KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    July 2019
    3305 pages
    ISBN:9781450362016
    DOI:10.1145/3292500
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    Published: 25 July 2019

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    Author Tags

    1. data mining
    2. representation learning
    3. urban computing

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    • National Science of Foundation China

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    KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2024)Generative Adversarial Networks: Applications, Challenges, and Open IssuesDeep Learning - Recent Findings and Research10.5772/intechopen.113098Online publication date: 29-May-2024
    • (2024)CGAPProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/832(7518-7526)Online publication date: 3-Aug-2024
    • (2024)HDM-GNN: A Heterogeneous Dynamic Multi-view Graph Neural Network for Crime PredictionACM Transactions on Sensor Networks10.1145/3665141Online publication date: 14-May-2024
    • (2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
    • (2024)A Novel Framework for Joint Learning of City Region Partition and RepresentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365285720:7(1-23)Online publication date: 17-Mar-2024
    • (2024)AdaTM: Fine-grained Urban Flow Inference with Adaptive Knowledge Transfer across Multiple CitiesProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679856(3424-3432)Online publication date: 21-Oct-2024
    • (2024)Urban Region Representation Learning with Attentive Fusion2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00336(4409-4421)Online publication date: 13-May-2024
    • (2024)SpatialScene2Vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluationInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2024.103743128(103743)Online publication date: Apr-2024
    • (2024)Towards effective urban region-of-interest demand modeling via graph representation learningData Mining and Knowledge Discovery10.1007/s10618-024-01049-438:6(3503-3530)Online publication date: 1-Nov-2024
    • (2023)Spatial-temporal graph learning with adversarial contrastive adaptationProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620133(41151-41163)Online publication date: 23-Jul-2023
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