From social networks such as Facebook, the World Wide Web and the Internet, to the
complex interactions between proteins in the cells of our bodies, we constantly face the …

Real-world networks, like social networks or the internet infrastructure, have structural
properties such as large clustering coefficients that can best be described in terms of an …

Nowadays, high-bandwidth networks are more easily accessible than ever before. However,
existing distributed graph-processing frameworks, such as GPS, fail to efficiently utilize the …

Hyperbolic geometry appears to be intrinsic in many large real networks. We construct and
implement a new maximum likelihood estimation algorithm that embeds scale-free graphs in …

Large real-world networks are typically scale-free. Recent research has shown that such
graphs are described best in a geometric space. More precisely, the Internet can be mapped …

Counting subgraphs is a fundamental analysis task for online social networks (OSNs). Given
the sheer size and restricted access of OSN, efficient computation of subgraph counts is …

Real-world networks, like social networks or the internet infrastructure, have structural
properties such as large clustering coefficients that can best be described in terms of an …

In Chung-Lu random graphs, a classic model for real-world networks, each vertex is
equipped with a weight drawn from a power-law distribution, and two vertices form an edge …

We consider a random graph model that was recently proposed as a model for complex
networks by Krioukov et al.(2010). In this model, nodes are chosen randomly inside a disk in …

Hyperbolic embedding aims to reveal the hidden space by capturing most of the properties
observed in real networks. Most existing hyperbolic embedding models map to learn the …