• Publications
  • Influence
Finding and evaluating community structure in networks.
  • M. Newman, M. Girvan
  • Computer Science
    Physical review. E, Statistical, nonlinear, and…
  • 11 August 2003
TLDR
It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Networks: An Introduction
TLDR
This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
Community structure in social and biological networks
  • M. Girvan, M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences…
  • 7 December 2001
TLDR
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
The Structure and Function of Complex Networks
  • M. Newman
  • Computer Science
    SIAM Rev.
  • 25 March 2003
TLDR
Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Power-Law Distributions in Empirical Data
TLDR
This work proposes a principled statistical framework for discerning and quantifying power-law behavior in empirical data by combining maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios.
Modularity and community structure in networks.
  • M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences…
  • 17 February 2006
TLDR
It is shown that the modularity of a network can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which is called modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times.
Finding community structure in very large networks.
TLDR
A hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure.
Fast algorithm for detecting community structure in networks.
  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and…
  • 22 September 2003
TLDR
An algorithm is described which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.
Finding community structure in networks using the eigenvectors of matrices.
  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and…
  • 10 May 2006
TLDR
A modularity matrix plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations, and a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong are proposed.
Power laws, Pareto distributions and Zipf's law
When the probability of measuring a particular value of some quantity varies inversely as a power of that value, the quantity is said to follow a power law, also known variously as Zipf's law or the
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