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

Due to the explosive rise of online social networks, social network analysis (SNA) has
emerged as a significant academic field in recent years. Understanding and examining …

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 …

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 …

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 …

A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …

In social networks, a node's position is, in and of itself, a form of social capital. Better-
positioned members not only benefit from (faster) access to diverse information, but innately …

The influence maximization paradigm has been used by researchers in various fields in
order to study how information spreads in social networks. While previously the attention …