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 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 …

Vital node identification is the problem of finding nodes of highest importance in complex
networks. This problem has crucial applications in various contexts such as viral marketing …

The potential for machine learning systems to amplify social inequities and unfairness is
receiving increasing popular and academic attention. Much recent work has focused on …

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …

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 …

Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

Influence maximization has found applications in a wide range of real-world problems, for
instance, viral marketing of products in an online social network, and propagation of …

Influence maximization is an optimization problem in the area of social graph analysis,
which asks to choose a subset of k individuals to maximize the number of influenced nodes …