Browsing Mathematics (Faculty of) by Subject "graph neural networks"
Now showing items 1-4 of 4
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Adaptive Cross-Project Bug Localization with Graph Learning
(University of Waterloo, 2022-06-07)Bug localization is the process of identifying the source code files associated with a bug report. This is important because it allows developers to focus their efforts on fixing the bugs than finding the root cause of ... -
JITGNN: A Deep Graph Neural Network for Just-In-Time Bug Prediction
(University of Waterloo, 2022-05-10)Just-In-Time (JIT) bug prediction is the problem of predicting software failure immediately after a change is submitted to the code base. JIT bug prediction is often preferred to other types of bug prediction (subsystem, ... -
On Using Embeddings for Ownership Verification of Graph Neural Networks
(University of Waterloo, 2023-08-11)Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as social networking. The primary goal of a ... -
Some Mathematical Perspectives of Graph Neural Networks
(University of Waterloo, 2022-05-12)Many real-world entities can be modelled as graphs, such as molecular structures, social networks, or images. Despite coming with such a great expressive power, the complex structure of graphs poses significant challenges ...