Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …

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 …

In today's increasingly connected world, graph mining plays a pivotal role in many real-world
application domains, including social network analysis, recommendations, marketing and …

Decision-making systems increasingly orchestrate our world: how to intervene on the
algorithmic components to build fair and equitable systems is therefore a question of utmost …

Purpose The purpose of this study is to examine the impact of information technologies and
technology-enabled social networks on the efficiency of knowledge management processes …

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 …

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 …

The problem of social influence maximization is widely applicable in designing viral
campaigns, news dissemination, or medical aid. State-of-the-art algorithms often select …

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