53 citations · 109 across the 14 of their papers we have counts for
14 papers
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes
Junwei Ma, Bo Li, Qingchun Li +2
The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spr…
Human Mobility Disproportionately Extends PM2.5 Emission Exposure for Low Income Populations
Chao Fan, Yu-Heng Chien, Ali Mostafavi
Ambient exposure to fine particulate matters of diameters smaller than 2.5μm (PM2.5) has been identified as one critical cause for respiratory disease. Disparities in exposure to P…
Quantitative Measures for Integrating Resilience into Transportation Planning Practice: Study in Texas
Cheng-Chun Lee, Akhil Rajput, Chia-Wei Hsu +10
The objective of this study is to propose a system-level framework with quantitative measures to assess the resilience of road networks. The framework proposed in this paper can he…
FMP: Toward Fair Graph Message Passing against Topology Bias
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), t…
Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities
Chao Fan, Yang Yang, Ali Mostafavi
In this study, we propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas and their interactions captured by hum…
Limitations of gravity models in predicting fine-scale spatial-temporal urban mobility networks
Chiawei Hsu, Chao Fan, Ali Mostafavi
This study identifies the limitations and underlying characteristics of urban mobility networks that influence the performance of the gravity model. The gravity model is a widely-u…