Graph learning for anomaly analytics: Algorithms, applications, and challenges
Anomaly analytics is a popular and vital task in various research contexts that has been
studied for several decades. At the same time, deep learning has shown its capacity in …
studied for several decades. At the same time, deep learning has shown its capacity in …
Exgc: Bridging efficiency and explainability in graph condensation
Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …
However, the associated computational and storage overheads raise concerns. In sight of …
Modeling spatio-temporal dynamical systems with neural discrete learning and levels-of-experts
In this paper, we address the issue of modeling and estimating changes in the state of the
spatio-temporal dynamical systems based on a sequence of observations like video frames …
spatio-temporal dynamical systems based on a sequence of observations like video frames …
The heterophilic snowflake hypothesis: Training and empowering gnns for heterophilic graphs
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based
learning tasks. Notably, most current GNN architectures operate under the assumption of …
learning tasks. Notably, most current GNN architectures operate under the assumption of …
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling
Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Credit card fraud poses a significant threat to the economy. While Graph Neural Network
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …
Data-unbalanced traffic accident prediction via adaptive graph and self-supervised learning
Traffic accident prediction is an important research problem, which can help to identify
dangerous situations on the road in advance and take appropriate measures. Nonetheless …
dangerous situations on the road in advance and take appropriate measures. Nonetheless …
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 …
Fortune favors the invariant: Enhancing GNNs' generalizability with Invariant Graph Learning
Generalizable and transferrable graph representation learning endows graph neural
networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless …
networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless …
A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline
RT Sukonna, SI Swapnil - arXiv preprint arXiv:2311.00634, 2023 - arxiv.org
Due to the stochastic nature of events, predicting the duration of a traffic incident presents a
formidable challenge. Accurate duration estimation can result in substantial advantages for …
formidable challenge. Accurate duration estimation can result in substantial advantages for …