Clustering Time-Evolving Networks Using the Spatio-Temporal Graph Laplacian

2024-07-12 · Maia Trower, Nataša Djurdjevac Conrad, Stefan Klus

Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varying graph structures is an important challenge. In this work, we generalize existing spectral clustering algorithms from static to dynamic graphs using canonical correlation analysis (CCA) to capture the temporal evolution of clusters. Based on this extended canonical correlation framework, we define the spatio-temporal graph Laplacian and investigate its spectral properties. We connect these concepts to dynamical systems theory via transfer operators, and illustrate the advantages of our method on benchmark graphs by comparison with existing methods. We show that the spatio-temporal graph Laplacian allows for a clear interpretation of cluster structure evolution over time for directed and undirected graphs.

📄 PDF Abstract BibTeX arXiv:2407.12864

Code (0)

등록된 구현이 없습니다.

Tasks

Clustering

Methods 이 논문이 사용한 방법론

Spectral Clustering Spectral clustering has attracted increasing attention due to the promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the…

B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data

2025-09-16 · Francis Ndikum Nji, Vandana Janaja, Jianwu Wang arxiv

Clustering high-dimensional multivariate spatiotemporal climate data is challenging due to complex temporal dependencies, evolving spatial interactions, and non-stationary dynamics. Conventional clustering methods, inclu…

CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data

2026-07-23 · Francis Ndikum Nji, Vandana Janeja, Jianwu Wang arxiv

Deep subspace clustering plays a critical role in applications involving multivariate spatiotemporal data, such as sea ice monitoring, disease spread analysis, and tracking neuro-degeneration over time. Despite recent ad…

Clustering of Time Series Data with Prior Geographical Information

2021-07-03 · Reza Asadi, Amelia Regan

Time Series data are broadly studied in various domains of transportation systems. Traffic data area challenging example of spatio-temporal data, as it is multi-variate time series with high correlations in spatial and t…

Anomaly Detection Clustering Time Series Time Series Analysis

AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting

2021-01-29 · Yi-Ju Lu, Cheng-Te Li

Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems. While recent advances…

Management Time Series Time Series Analysis

Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data

2024-09-19 · Francis Ndikum Nji, Omar Faruque, Mostafa Cham, Janeja Vandana 외

Classifying subsets based on spatial and temporal features is crucial to the analysis of spatiotemporal data given the inherent spatial and temporal variability. Since no single clustering algorithm ensures optimal resul…

Clustering Diversity Graph Attention