A review of graph neural networks in epidemic modeling
Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …
epidemiological models. Traditional mechanistic models mathematically describe the …
Knowledge distillation on graphs: A survey
Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …
capability to handle graph data. However, they are difficult to be deployed in resource …
Class-imbalanced learning on graphs: A survey
The rapid advancement in data-driven research has increased the demand for effective
graph data analysis. However, real-world data often exhibits class imbalance, leading to …
graph data analysis. However, real-world data often exhibits class imbalance, leading to …
Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks
Graphs have emerged as a natural choice to represent and analyze the intricate patterns
and rich information of the Web, enabling applications such as online page classification …
and rich information of the Web, enabling applications such as online page classification …
Resilience analysis for confronting the spreading risk of contagious diseases
Spreading risks of contagious diseases have threatened human lives and disrupted the
global economy. Protecting humanity from contagious diseases, such as coronavirus, is a …
global economy. Protecting humanity from contagious diseases, such as coronavirus, is a …
Rethinking sensors modeling: Hierarchical information enhanced traffic forecasting
With the acceleration of urbanization, traffic forecasting has become an essential role in
smart city construction. In the context of spatio-temporal prediction, the key lies in how to …
smart city construction. In the context of spatio-temporal prediction, the key lies in how to …
Modeling Spatio-Temporal Mobility across Data Silos via Personalized Federated Learning
Spatio-temporal mobility modeling plays a pivotal role in the advancement of mobile
computing. Nowadays, data is frequently held by various distributed silos, which are isolated …
computing. Nowadays, data is frequently held by various distributed silos, which are isolated …
Generating Daily Activities with Need Dynamics
Daily activity data recording individuals' various activities in daily life are widely used in
many applications such as activity scheduling, activity recommendation, and policymaking …
many applications such as activity scheduling, activity recommendation, and policymaking …
[HTML][HTML] iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices
The emergence of pandemics poses a persistent threat to both global health and economic
stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early …
stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early …
Adaptive and Interactive Multi-Level Spatio-Temporal Network for Traffic Forecasting
Traffic forecasting is a challenging research topic due to the complex spatial and temporal
dependencies among different roads. Though great efforts have been made on traffic …
dependencies among different roads. Though great efforts have been made on traffic …