SIGKDD

Network data has become widespread, larger, and more complex over the years. Traditional network data is dyadic, capturing the relations among pairs of entities. With the need to model interactions among more than two entities, significant research has ...

Recent advances in deep learning have demonstrated the ability of learning-based methods to tackle very hard downstream tasks. Historically, this has been demonstrated in predictive tasks, while tasks more akin to the traditional KDD (Knowledge Discovery ...

Most classification (supervised learning) algorithms optimise a single objective, typically the predictive performance of the learned classification model. However, in high-stake classification applications, involving e.g. decisions about whether or not ...

Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead ...

Currently, privacy risks assessment is mainly performed as audits conducted by data privacy analysts. In the TAILOR project, we promote a more systematic and automatic approach based on interpretable metrics and formal methods to evaluate privacy risks ...