Fast, open, free, and accessible, online social networks are massively used to share news
and various information. Unfortunately, their explosive growth amplifies the dissemination of …

Cardinal objectives serve as intuitive targets in fair machine learning by summarizing utility
(welfare) or disutility (malfare) $ u $ over $ g $ groups. Under standard axioms, all welfare …

Can we employ one neural model to efficiently dismantle many complex yet unique
networks? This article provides an affirmative answer. Diverse real-world systems can be …

In network analysis, the betweenness centrality of a node informally captures the fraction of
shortest paths visiting that node. The computation of the betweenness centrality measure is …

Betweenness centrality is one of the key measures of the node importance in a network.
However, it is computationally intractable to calculate the exact betweenness centrality of …

The identification of the set of k most central nodes of a graph, or centrality maximization, is a
key task in network analysis, with various applications ranging from finding communities in …

“I'm an MC still as honest”–Eminem, Rap God We present MCRapper, an algorithm for
efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families …

Betweenness centrality (BC), a classic measure which quantifies the importance of a vertex
to act as a communication" bridge" between other vertices in the network, is widely used in …

Betweenness centrality of a vertex in a graph evaluates how often the vertex occurs in the
shortest paths. It is a widely used metric of vertex importance in graph analytics. While …

“Sim Sala Bim!”—Silvan, https://en. wikipedia. org/wiki/Silvan_ (illusionist) Betweenness
centrality is a popular centrality measure with applications in several domains and whose …