We propose a novel model-selection method for dynamic networks. Our approach involves
training a classifier on a large body of synthetic network data. The data is generated by …

Recently there has been increased interest in fitting generative graph models to real-world
networks. In particular, Bläsius et al. have proposed a framework for systematic evaluation of …

Nowadays, blockchain is an upcoming area for researchers from different research fields.
Bitcoin, as the first successful cryptocurrency, has accumulated numerous data after its …

Data-driven analysis of complex networks has been in the focus of research for decades. An
important area of research is to study how well real networks can be described with a small …

We study push-pull rumour spreading in small-world models for social networks where the
degrees follow a power-law. In a non-geometric setting Fountoulakis, Panagiotou and …

Random graph models are widely used to understand network properties and graph
algorithms. Key to such analyses are the different parameters of each model, which affect …

Data-driven analysis of large social networks has attracted a great deal of research interest.
In this paper, we investigate 120 real social networks and their measurement-calibrated …

In this paper, we conducted an experiment for comparison of the graphs generated by Erdős-
Rényi, Barabási-Albert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models and a web …

Network science is driven by the question which properties large real-world networks have
and how we can exploit them algorithmically. In the past few years, hyperbolic graphs have …

Classification is a fundamental problem in machine learning, data science, and statistics,
and it involves assigning a class label to an instance based on its features. Classification is …