Collective Embedding with Feature Importance: A Unified Approach for Spatiotemporal Network Embedding
Abstract
In the last decade, there has been great progress in the field of machine learning and deep learning. These models have been instrumental in addressing a great number of problems. However, they have struggled when it comes to dealing with high dimensional data. In recent years, representation learning models have proven to be quite efficient in addressing this problem as they are capable of capturing effective lower-dimensional representations of the data. However, most of the existing models are quite ineffective when it comes to dealing with high dimensional spatiotemporal data as they encapsulate complex spatial and temporal relationships that exist among real-world objects. High-dimensional spatiotemporal data of cities represent urban communities. By learning their social structure we can better quantitatively depict them and understand factors influencing rapid growth, expansion, and changes. In this paper, we propose a collective embedding framework that leverages the use of auto-encoders and Laplacian score to learn effective embeddings of spatiotemporal networks of urban communities. In addition, we also develop a weighted degree centrality measure for constructing spatiotemporal heterogeneous networks. To evaluate the performance of our proposed model, we implement it on real-world urban community data. Experimental results demonstrate the effectiveness of our model over state-of-the-art alternatives.
Recommended Citation
D. Keerthi Chandra et al., "Collective Embedding with Feature Importance: A Unified Approach for Spatiotemporal Network Embedding," International Conference on Information and Knowledge Management, Proceedings, pp. 615 - 624, Oct 2020.
The definitive version is available at https://doi.org/10.1145/3340531.3412030
Meeting Name
International Conference on Information and Knowledge Management
Department(s)
Computer Science
Keywords and Phrases
autoencoder; laplacian score; network embedding; representation learning; spatiotemporal heterogeneous network; weighted degree centrality measure
International Standard Book Number (ISBN)
978-145036859-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020, All rights reserved.
Publication Date
19 Oct 2020
