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Found 11 papers, 5 papers with code
GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation
As analysts create their spatial models, our framework flags potential issues with model parameter selections, utilizes template-based text generation to summarize model outputs, and links with external knowledge repositories to provide annotations that help to explain the model results.
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
We systematically review related works from the data to the computational aspects.
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.
DISCO: Comprehensive and Explainable Disinformation Detection
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets.
Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
In this paper, we utilized generative models, and reformulate it for problems in molecular dynamics (MD) simulation, by introducing an MD potential energy component to our generative model.
Deeper-GXX: Deepening Arbitrary GNNs
However, two major problems hinder the deeper GNNs to obtain satisfactory performance, i. e., vanishing gradient and over-smoothing.
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Metric Learning on Temporal Graphs via Few-Shot Examples
Graph metric learning methods aim to learn the distance metric over graphs such that similar graphs are closer and dissimilar graphs are farther apart.
InfoFair: Information-Theoretic Intersectional Fairness
The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e. g., gender, race, marital status, etc.)
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.
Diagnosing Concept Drift with Visual Analytics
Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate.
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks.
