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

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.

Meeting Name

25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 (2019: Aug. 4-8, Anchorage, AK)

Department(s)

Computer Science

Keywords and Phrases

Data Mining; Representation Learning; Urban Computing

International Standard Book Number (ISBN)

978-145036201-6

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Aug 2019

Link to Full Text

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