In several real-world applications (e.g., online advertising, item recommendations, etc.) it may not be possible to release and share the real dataset due to privacy concerns. As a result, synthetic data generation (SDG) has emerged as a promising solution for data sharing. While the main goal of private SDG is to create a dataset that preserves the privacy of individuals contributing to the dataset, the use of synthetic data also creates an opportunity to improve fairness. Since there often exist historical biases in the datasets, using the original real data for training can lead to an unfair model. Using synthetic data, we can attempt to remove such biases from the dataset before releasing the data. In this work, we formalize the definition of fairness in synthetic data generation and provide a general framework to achieve fairness. Then we consider two notions of counterfactual fairness and information filtering fairness and show how our framework can be used for these definitions.

Denoising diffusion generative models are state-of-the-art methods for generating synthetic images that have also proved successful in tabular and graph synthetic data generation. However, their computational complexity has limited the application of these techniques to graph data, focusing usually on smaller graphs, such as those used in molecular modeling. In this paper, we propose SaGess, a discrete denoising diffusion approach, which is able to generate large real-world networks. Through a generalized divide-and-conquer framework, SaGess overcomes the scaling limitations of the diffusion model DiGress, by sampling a covering of subgraphs of the initial graph, training a DiGress module, and finally reconstructing a synthetic graph using the subgraphs that have been generated using the DiGress module. We evaluate the quality of the synthetic data sets against several competitor methods by comparing graph statistics between the original and synthetic samples, as well as evaluating the utility of the synthetic data set produced by using it to train a task-driven model, namely link prediction. In our experiments, SaGess outperforms most of the one-shot state-of-the-art graph generating methods by a significant factor, both on the graph metrics and on the link prediction task.

We propose a new form of plausible counterfactual explanation designed to explain the behaviour of computer vision systems used in urban analytics that make predictions based on properties across the entire image, rather than specific regions of it. We illustrate the merits of our approach by explaining computer vision models used to analyse street imagery, which are now widely used in GeoAI and urban analytics. Such explanations are important in urban analytics as researchers and practioners are increasingly reliant on it for decision making. Finally, we perform a user study that demonstrate our approach can be used by non-expert users, who might not be machine learning experts, to be more confident and to better understand the behaviour of image-based classifiers/regressors for street view analysis. Furthermore, the method can potentially be used as an engagement tool to visualise how public spaces can plausibly look like. The limited realism of the counterfactuals is a concern which we hope to improve in the future.

We present a simple regularization of adversarial perturbations based upon the perceptual loss. While the resulting perturbations remain imperceptible to the human eye, they differ from existing adversarial perturbations in that they are semi-sparse alterations that highlight objects and regions of interest while leaving the background unaltered. As a semantically meaningful adverse perturbations, it forms a bridge between counterfactual explanations and adversarial perturbations in the space of images.We evaluate our approach on several standard explainability benchmarks, namely, weak localization, insertion-deletion, and the pointing game demonstrating that perceptually regularized counterfactuals are an effective explanation for image-based classifiers.

Motivation: Our work is motivated by an interest in constructing a protein-protein interaction network that captures key features associated with Parkinson's disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation. Results: We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork; in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein-protein interaction network. Availability and implementation: The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.