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

Evolving networks by definition are networks that change as a function of time. They are a
natural extension of network science since almost all real-world networks evolve over time …

In complex networks, each node has some unique characteristics that define the importance
of the node based on the given application-specific context. These characteristics can be …

ABPA Ξ A Σ (ABRAXAS): Gnostic word of mystic meaning. We present ABRA, a suite of
algorithms to compute and maintain probabilistically guaranteed high-quality …

Graphs arise naturally in numerous situations, including social graphs, transportation
graphs, web graphs, protein graphs, etc. One of the important problems in these settings is …

Betweenness centrality is a classic measure that quantifies the importance of a graph
element (vertex or edge) according to the fraction of shortest paths passing through it. This …

Measures of centrality of vertices in a network are usually defined solely on the basis of the
network structure. In highly dynamic networks, where vertices appear and disappear and …

The closeness centrality is a well-known measure of importance of a vertex within a given
complex network. Having high closeness centrality can have positive impact on the vertex …

Betweenness centrality quantifies the importance of nodes in a graph in many applications,
including network analysis, community detection and identification of influential users …

Betweenness centrality ranks the importance of nodes by their participation in all shortest
paths of the network. Therefore computing exact betweenness values is impractical in large …