Large graphs are becoming ubiquitous, presenting significant computational hurdles in data processing and analysis. Graph Coarsening algorithms are frequently employed to condense large graphs while preserving key graph properties. Real-world graphs also have features or contexts associated with each node. However, existing coarsening methods often overlook simultaneity across node features and structural information. Recent approaches to alleviate this limitation are computationally intensive, and primarily suited for homophilic datasets. Most existing approaches are unsuitable for streaming and evolving graphs, as they require recomputation of the coarsened graph at every timestamp. In this paper, we introduce a Fast and Scalable Hashing-Based Universal Graph Coarsening (UGC) Framework, that integrates locality-sensitive hashing, and feature augmentation to effectively coarsen graphs. UGC is exceptionally fast, straightforward to implement, and capable of handling homophilic, heterophilic, and streaming graphs making it a truly universal solution for graph coarsening. We use an optimization-based framework to minimize a constrained $\epsilon$ε similarity between the original and coarsened graphs, where $\epsilon$ε is between zero and one. Through extensive experimentation on real and synthetic datasets, we demonstrate the effectiveness of our approach in terms of improved runtime complexity and generalization to heterophilic and streaming graphs. Furthermore, we showcase its utility in downstream tasks, emphasizing its scalability for training graph neural networks on coarsened graphs from benchmark real-world datasets.

Graph neural networks for hyperbolic space has emerged as a powerful tool for embedding datasets exhibiting a highly non-Euclidean latent anatomy e.g., graphs with hierarchical structures. While several Hyperbolic Graph Neural Networks (Hy-GNNs) have been developed to enhance the representation of hierarchical datasets, they remain susceptible to noise and adversarial attacks, posing serious risks in critical applications. The absence of robust Hy-GNN frameworks underscores a pressing problem. This research addresses this challenge by introducing HyperDefender—a robust and flexible approach designed to fortify Hy-GNNs against adversarial attacks and noises. HyperDefender aims to secure the reliability of applications that depend on the integrity of hierarchical graph-structured data in real-world scenarios. Experimental results demonstrate that HyperDefender significantly improves node classification accuracy across various attacks, effectively mitigating the performance degradation typically observed in Hy-GNNs when the hierarchy in original datasets is compromised.

Although machine learning (ML) has significant potential to improve healthcare decision-making, embedded biases in algorithms and datasets risk exacerbating health disparities across demographic groups. To address this challenge, it is essential to rigorously evaluate bias mitigation strategies to ensure fairness and reliability across patient populations. The aim of this research is to propose a comprehensive evaluation framework that systematically assesses a wide range of bias mitigation techniques at pre-processing, in-processing, and post-processing stages, using both single- and multi-stage intervention approaches. This study evaluates bias mitigation strategies across three clinical prediction tasks: breast cancer diagnosis, stroke prediction, and Alzheimer’s disease detection. Our evaluation employs group- and individual-level fairness metrics, contextualized for specific sensitive attributes relevant to each dataset. Beyond fairness-accuracy trade-offs, we demonstrate how metric selection must align with clinical goals (e.g., parity metrics for equitable access, confusion-matrix metrics for diagnostics). Our results reinforce that no single classifier or mitigation strategy is universally optimal, underscoring the value of our proposed framework for evaluating fairness and accuracy throughout the bias mitigation process. According to the results, Adversarial Debiasing improved fairness by 95% in breast cancer diagnosis without compromising accuracy. Reweighing was most effective in stroke prediction, boosting fairness by 41%, and Reject Option Classification yielded nearly 50% fairness improvement in Alzheimer’s detection. Multi-stage bias mitigation did not consistently lead to better outcomes, and in many cases, fairness gains came at the expense of accuracy. These findings provide practical guidance for selecting fairness-aware machine learning strategies in healthcare, aiding both model development and benchmarking across diverse clinical applications. • We propose a comprehensive evaluation framework that systematically compares single- and multi-stage bias mitigation strategies across pre-, in-, and post-processing stages in healthcare machine learning. • The framework assesses accuracy–fairness trade-off using task-sensitive fairness metrics across three clinical prediction tasks, breast cancer diagnosis, stroke prediction, and Alzheimer’s disease detection, demonstrating real-world applicability. • We empirically validate multiple bias mitigation strategies, showing that significant fairness improvements can be achieved while maintaining high diagnostic accuracy.