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RelEx: A Model-Agnostic Relational Model Explainer
In this work, we develop RelEx, a model-agnostic relational explainer to explain black-box relational models with only access to the outputs of the black-box.
Learning Fairness-aware Relational Structures
The development of fair machine learning models that effectively avert bias and discrimination is an important problem that has garnered attention in recent years.
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors
Further, our model is capable of learning latent characteristics in real-world networks via meaningful latent variables encoded as a complex combination of observed features and membership distributions.
Adversarial Model Extraction on Graph Neural Networks
Along with the advent of deep neural networks came various methods of exploitation, such as fooling the classifier or contaminating its training data.
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