Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

Introduction Knowledge graphs have proven to be promising systems of information storage
and retrieval. Due to the recent explosion of heterogeneous multimodal data sources …

Graph representation learning has become a ubiquitous component in many scenarios,
ranging from social network analysis to energy forecasting in smart grids. In several …

Conventional graph neural networks (GNNs) are often confronted with fairness issues that
may stem from their input, including node attributes and neighbors surrounding a node …

Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
Despite the successes of GCN deployment, GCN often exhibits performance disparity with …

As machine learning algorithms are increasingly deployed for high-impact automated
decision making, ethical and increasingly also legal standards demand that they treat all …

Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in
many real-world application domains where decisions can have a strong societal impact …

Graph embedding techniques are pivotal in real-world machine learning tasks that operate
on graph-structured data, such as social recommendation and protein structure modeling …

In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

Abstract Machine learning and data mining algorithms have been increasingly used recently
to support decision-making systems in many areas of high societal importance such as …