Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data
Multi-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is the time series of mobility when periodicity is considered. The graphs are analyzed using standard signal proc...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of Signal Processing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10840315/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859612089057280 |
|---|---|
| author | Hirotaka Kaji Kazushi Ikeda |
| author_facet | Hirotaka Kaji Kazushi Ikeda |
| author_sort | Hirotaka Kaji |
| collection | DOAJ |
| description | Multi-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is the time series of mobility when periodicity is considered. The graphs are analyzed using standard signal processing methods such as singular value decomposition and tensor analysis, which can estimate missing values. However, their feature extraction abilities are insufficient for optimizing mobility networks. This study proposes a method that combines the Wasserstein non-negative matrix factorization (W-NMF) with line graphs to obtain low-dimensional representations of multi-layered graphs. A line graph is defined as the dual graph of a graph, where the vertices correspond to the edges of the original graph, and the edges correspond to the vertices. Thus, the shortest path length between two vertices in the line graph corresponds to the distance between the edges in the original graph. Through experiments using synthetic and benchmark datasets, we show that the performance and robustness of our method are superior to conventional methods. Additionally, we apply our method to real-world taxi origin—destination data as a mobility dataset and discuss the findings. |
| format | Article |
| id | doaj-art-62236cfd1dfb4f7b8a4ec78774cb5dc8 |
| institution | Kabale University |
| issn | 2644-1322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj-art-62236cfd1dfb4f7b8a4ec78774cb5dc82025-02-11T00:01:47ZengIEEEIEEE Open Journal of Signal Processing2644-13222025-01-01619420210.1109/OJSP.2025.352886910840315Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility DataHirotaka Kaji0https://orcid.org/0000-0001-8382-7414Kazushi Ikeda1https://orcid.org/0000-0003-3330-6121Frontier Research Center, Toyota Motor Corporation, Susono, Shizuoka, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, JapanMulti-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is the time series of mobility when periodicity is considered. The graphs are analyzed using standard signal processing methods such as singular value decomposition and tensor analysis, which can estimate missing values. However, their feature extraction abilities are insufficient for optimizing mobility networks. This study proposes a method that combines the Wasserstein non-negative matrix factorization (W-NMF) with line graphs to obtain low-dimensional representations of multi-layered graphs. A line graph is defined as the dual graph of a graph, where the vertices correspond to the edges of the original graph, and the edges correspond to the vertices. Thus, the shortest path length between two vertices in the line graph corresponds to the distance between the edges in the original graph. Through experiments using synthetic and benchmark datasets, we show that the performance and robustness of our method are superior to conventional methods. Additionally, we apply our method to real-world taxi origin—destination data as a mobility dataset and discuss the findings.https://ieeexplore.ieee.org/document/10840315/Graph analysismobility datanonnegative matrix factorizationWasserstein distance |
| spellingShingle | Hirotaka Kaji Kazushi Ikeda Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data IEEE Open Journal of Signal Processing Graph analysis mobility data nonnegative matrix factorization Wasserstein distance |
| title | Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data |
| title_full | Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data |
| title_fullStr | Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data |
| title_full_unstemmed | Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data |
| title_short | Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data |
| title_sort | wasserstein non negative matrix factorization for multi layered graphs and its application to mobility data |
| topic | Graph analysis mobility data nonnegative matrix factorization Wasserstein distance |
| url | https://ieeexplore.ieee.org/document/10840315/ |
| work_keys_str_mv | AT hirotakakaji wassersteinnonnegativematrixfactorizationformultilayeredgraphsanditsapplicationtomobilitydata AT kazushiikeda wassersteinnonnegativematrixfactorizationformultilayeredgraphsanditsapplicationtomobilitydata |