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Network Analysis: Methodological Foundations
Ulrik Brandes,Thomas Erlebach +1 more
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This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually calculating centrality indices.Abstract:
Fundamentals.- I Elements.- Centrality Indices.- Algorithms for Centrality Indices.- Advanced Centrality Concepts.- II Groups.- Local Density.- Connectivity.- Clustering.- Role Assignments.- Blockmodels.- Network Statistics.- Network Comparison.- Network Models.- Spectral Analysis.- Robustness and Resilience.read more
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Computing communities in large networks using random walks
Pascal Pons,Matthieu Latapy +1 more
TL;DR: In this article, a measure of similarities between vertices based on random walks is proposed, which has several important advantages: it captures well the community structure in a network, it can be computed efficiently, it works at various scales.
Journal ArticleDOI
Molecular ecological network analyses
TL;DR: A novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods is described, which provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes.
Journal ArticleDOI
On Modularity Clustering
Ulrik Brandes,Daniel Delling,Marco Gaertler,Robert Görke,Martin Hoefer,Zoran Nikoloski,Dorothea Wagner +6 more
TL;DR: The conjectured hardness of maximizing modularity both in the general case and with the restriction to cuts is proved and an Integer Linear Programming formulation is given.
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The human connectome: a complex network
TL;DR: Current empirical efforts toward generating a network map of the human brain, the human connectome, are reviewed, and how the connectome can provide new insights into the organization of the brain's structural connections and their role in shaping functional dynamics are explored.
Journal ArticleDOI
A Survey of Statistical Network Models
TL;DR: In this paper, the authors provide an overview of the historical development of statistical network modeling and then introduce a number of examples that have been studied in the network literature and their subsequent discussion focuses on some prominent static and dynamic network models and their interconnections.