Search Results for author: M. E. J. Newman

Found 21 papers, 13 papers with code

20 years of network community detection

no code implementations30 Jul 2022 Santo Fortunato, M. E. J. Newman

A fundamental technical challenge in the analysis of network data is the automated discovery of communities - groups of nodes that are strongly connected or that share similar features or roles.

Community Detection

Efficient computation of rankings from pairwise comparisons

no code implementations30 Jun 2022 M. E. J. Newman

We study the ranking of individuals, teams, or objects, based on pairwise comparisons between them, using the Bradley-Terry model.

Ranking with multiple types of pairwise comparisons

no code implementations27 Jun 2022 M. E. J. Newman

The task of ranking individuals or teams, based on a set of comparisons between pairs, arises in various contexts, including sporting competitions and the analysis of dominance hierarchies among animals and humans.

Belief propagation for networks with loops

no code implementations23 Sep 2020 Alec Kirkley, George T. Cantwell, M. E. J. Newman

Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of networks that contain short loops.

Bayesian inference of network structure from unreliable data

1 code implementation7 Aug 2020 Jean-Gabriel Young, George T. Cantwell, M. E. J. Newman

Most empirical studies of complex networks do not return direct, error-free measurements of network structure.

Social and Information Networks Physics and Society Applications

Improved mutual information measure for classification and community detection

no code implementations29 Jul 2019 M. E. J. Newman, George T. Cantwell, Jean Gabriel Young

The information theoretic quantity known as mutual information finds wide use in classification and community detection analyses to compare two classifications of the same set of objects into groups.

Classification Community Detection +1

Mixing patterns and individual differences in networks

1 code implementation2 Oct 2018 George T. Cantwell, M. E. J. Newman

We study mixing patterns in networks, meaning the propensity for nodes of different kinds to connect to one another.

Social and Information Networks Physics and Society

Community detection in networks: Modularity optimization and maximum likelihood are equivalent

1 code implementation7 Jun 2016 M. E. J. Newman

We demonstrate an exact equivalence between two widely used methods of community detection in networks, the method of modularity maximization in its generalized form which incorporates a resolution parameter controlling the size of the communities discovered, and the method of maximum likelihood applied to the special case of the stochastic block model known as the planted partition model, in which all communities in a network are assumed to have statistically similar properties.

Social and Information Networks Physics and Society

Structure and inference in annotated networks

no code implementations14 Jul 2015 M. E. J. Newman, Aaron Clauset

For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network, geographic location of nodes in the Internet, or cellular function of nodes in a gene regulatory network.

Community Detection

Prediction of highly cited papers

1 code implementation30 Oct 2013 M. E. J. Newman

In an article written five years ago [arXiv:0809. 0522], we described a method for predicting which scientific papers will be highly cited in the future, even if they are currently not highly cited.

Physics and Society Digital Libraries Social and Information Networks

Coauthorship and citation in scientific publishing

1 code implementation1 Apr 2013 Travis Martin, Brian Ball, Brian Karrer, M. E. J. Newman

A large number of published studies have examined the properties of either networks of citation among scientific papers or networks of coauthorship among scientists.

Digital Libraries Social and Information Networks Physics and Society

An efficient and principled method for detecting communities in networks

1 code implementation18 Apr 2011 Brian Ball, Brian Karrer, M. E. J. Newman

We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times.

Social and Information Networks Statistical Mechanics Physics and Society

Power-law distributions in empirical data

7 code implementations7 Jun 2007 Aaron Clauset, Cosma Rohilla Shalizi, M. E. J. Newman

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena.

Data Analysis, Statistics and Probability Disordered Systems and Neural Networks Applications Methodology

Mixture models and exploratory analysis in networks

no code implementations15 Nov 2006 M. E. J. Newman, E. A. Leicht

Networks are widely used in the biological, physical, and social sciences as a concise mathematical representation of the topology of systems of interacting components.

Data Analysis, Statistics and Probability Statistical Mechanics Physics and Society

Modularity and community structure in networks

1 code implementation17 Feb 2006 M. E. J. Newman

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules.

Data Analysis, Statistics and Probability Statistical Mechanics Physics and Society

Finding community structure in very large networks

no code implementations9 Aug 2004 Aaron Clauset, M. E. J. Newman, Cristopher Moore

Here we present 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(m d log n) where d is the depth of the dendrogram describing the community structure.

Statistical Mechanics Disordered Systems and Neural Networks

The statistical mechanics of networks

1 code implementation25 May 2004 Juyong Park, M. E. J. Newman

We study the family of network models derived by requiring the expected properties of a graph ensemble to match a given set of measurements of a real-world network, while maximizing the entropy of the ensemble.

Statistical Mechanics Disordered Systems and Neural Networks

Community structure in social and biological networks

1 code implementation7 Dec 2001 Michelle Girvan, M. E. J. Newman

We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.

Statistical Mechanics Disordered Systems and Neural Networks

Clustering and preferential attachment in growing networks

1 code implementation11 Apr 2001 M. E. J. Newman

We study empirically the time evolution of scientific collaboration networks in physics and biology.

Statistical Mechanics

A fast Monte Carlo algorithm for site or bond percolation

1 code implementation18 Jan 2001 M. E. J. Newman, R. M. Ziff

We describe in detail a new and highly efficient algorithm for studying site or bond percolation on any lattice.

Statistical Mechanics

Glassy dynamics and aging in an exactly solvable spin model

1 code implementation25 Jul 1997 M. E. J. Newman, Cristopher Moore

Instead, it falls out of equilibrium at a temperature which decreases logarithmically as a function of the cooling time.

Statistical Mechanics

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