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Collective Representation Learning on Spatiotemporal Heterogeneous Information Networks

Published: 05 November 2019 Publication History

Abstract

Representation learning is a technique that is used to capture the underlying latent features of complex data. Representation learning on networks has been widely implemented for learning network structure and embedding it in a low dimensional vector space. In recent years, network embedding using representation learning has attracted increasing attention, and many deep architectures have been widely proposed. However, existing network embedding techniques ignore the multi-class spatial and temporal relationships that crucially reflect the complex nature among vertices and links in spatiotemporal heterogeneous information networks(SHINs).
To address this problem, in this paper, we present two types of collective representation learning models for spatiotemporal heterogeneous information network embedding (SHNE). 1) We propose a model called Multilingual SHNE (M-SHNE); the proposed model leverages the use of random walks along with multilingual word embedding technique used in natural language processing (NLP) to collectively learn the spatiotemporal proximity measures between vertices in SHINs and preserve it in a low dimensional vector space. 2) We propose a second method called Meta path Constrained Random walk SHNE (MCR-SHNE) that combines the advantage of meta path counting algorithm, path constrained random walks, and word embedding technique to generate lower dimensional embeddings that preserve the spatiotemporal proximity measures in SHINs. Experimental results demonstrate the effectiveness of our two proposed models over state-of-the-art algorithms on real-world datasets.

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  1. Collective Representation Learning on Spatiotemporal Heterogeneous Information Networks

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    cover image ACM Conferences
    SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2019
    648 pages
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    Published: 05 November 2019

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    Author Tags

    1. Semantic representation
    2. distributional semantics
    3. meta paths
    4. proximity measures
    5. random walks

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    SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
    Overall Acceptance Rate 257 of 1,238 submissions, 21%

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    • (2023)Self-Supervised Representation Learning for Geographical Data—A Systematic Literature ReviewISPRS International Journal of Geo-Information10.3390/ijgi1202006412:2(64)Online publication date: 12-Feb-2023
    • (2023)Understanding Urban Economic Status through GNN-based Urban Representation Learning Using Mobility DataProceedings of the 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI10.1145/3615900.3628786(71-80)Online publication date: 13-Nov-2023
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