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Title: Discovering functionality of urban regions by learning low-dimensional representations of a spatial multiplex network
The complex relationships in an urban environment can be captured through multiple interrelated sources of data. These relationships form multilayer networks, that are also spatially embedded in an area, could be used to identify latent patterns. In this work, we propose a low-dimensional representation learning approach that considers multiple layers of a multiplex network simultaneously and is able to encode similarities between nodes across different layers. In particular, we introduce a novel neural network architecture to jointly learn low-dimensional representations of each network node from multiple layers of a network. This process simultaneously fuses knowledge of various data sources to better capture the characteristics of the nodes. To showcase the proposed method we focus on the problem of identifying the functionality of an urban region. Using a variety of public data sources for New York City, we design a multilayer network and evaluate our approach. Our results indicate that our proposed approach can improve the accuracy of traditional approaches in an unsupervised task.
Authors:
; ;
Award ID(s):
1739413
Publication Date:
NSF-PAR ID:
10074570
Journal Name:
Proceedings of the Third Mining Urban Data Workshop (MUD 2018)
Sponsoring Org:
National Science Foundation
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