The growing importance of understanding and addressing algorithmic bias in artificial
intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

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 …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Several behavioral, social, and public health interventions, such as suicide/HIV prevention
or community preparedness against natural disasters, leverage social network information to …

The problem of selecting a subset of nodes with greatest influence in a graph, commonly
known as influence maximization, has been well studied over the past decade. This problem …

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

The influence maximization problem (IMP) has been one of the most attractive topics in the
field of social networks. However, sometimes fairness in IMP should be considered …

Influence maximization is a widely studied topic in network science, where the aim is to
reach the maximum possible number of nodes, while only targeting a small initial set of …

Submodular maximization has become established as the method of choice for the task of
selecting representative and diverse summaries of data. However, if datapoints have …