Module identification in bipartite and directed networks

Guimerà, Roger; Sales‐Pardo, Marta; Amaral, Luı́s A. Nunes

doi:10.1103/physreve.76.036102

Cited by 378 publications

(403 citation statements)

References 49 publications

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“…Network decompositions may reveal certain underlying architectures and interesting methods to detect modularity have been developed recently [11,16,19,21,[37][38][39]. We also calculated the optimal modularity of the Linux kernel networks, using Newman's eigenvector-based algorithms for both undirected [38] and directed networks [39], and found unclear trends during the same …”

Section: Resultsmentioning

confidence: 99%

Detecting evolving patterns of self‐organizing networks by flow hierarchy measurement

Luo

Magee

2011

Complexity

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Hierarchies occur widely in evolving self-organizing ecological, biological, technological and social networks, but detecting and comparing hierarchies is difficult. Here we present a metric and technique to quantitatively assess the extent to which self-organizing directed networks exhibit a flow hierarchy. Flow hierarchy is a commonly observed but theoretically overlooked form of hierarchy in networks. We show that the ecological, neurobiological, economic and information processing networks are generally more hierarchical than their comparable random networks. We further discovered that hierarchy degree has increased over the course of the evolution of Linux kernels, confirming an early hypothesis by Herbert Simon on the emergence of hierarchy in evolutionary processes. Taken together, our results suggest that hierarchy is a central organizing feature of real-world evolving networks, and the measurement of hierarchy opens the way to understand the structural regimes and evolutionary patterns of self-organizing networks. Our measurement technique makes it possible to objectively compare hierarchies of different networks and of different evolutionary stages of a single network, and compare evolving patterns of different networks. It can be applied to various complex systems, which can be represented as directed networks.

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Section: Resultsmentioning

confidence: 99%

Detecting evolving patterns of self‐organizing networks by flow hierarchy measurement

Luo

Magee

2011

Complexity

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“…Under this assumption, one can use one of the many well-performing algorithms for community detection reported in the literature [25,47,48]. We choose here to use the Infomap algorithm developed by Rosvall and Bergstrom [44].…”

Section: A Network Approach To Topic Modelingmentioning

confidence: 99%

High-Reproducibility and High-Accuracy Method for Automated Topic Classification

et al. 2015

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Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

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“…6b, as a function of p/p dir c . In order to check the theoretical prediction for the critical point obtained in (38) we have carried out a finite-size scaling analysis of the numerical results. In Fig.…”

Section: Percolation Transition In the Directed E-r Graphmentioning

confidence: 99%

“…identify meaningful groups of customers (users), or support biomedical researchers in their search for individual target molecules and novel protein complex targets [47,4]. Since communities have no widely accepted unique definition, the number of available methods to pinpoint them is vast [74,76,26,46,32,54,73,64,27,67,71,72,37,36,38,52]. The majority of these algorithms classify the nodes into disjoint communities, and in most cases a global quantity called modularity [56,55] is used to evaluate the quality of the partitioning.…”

Section: Applications: Community Finding and Clusteringmentioning

confidence: 99%

k-Clique Percolation and Clustering

Palla

Ábel

Farkas

et al. 2008

Bolyai Society Mathematical Studies

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We summarise recent results connected to the concept of k-clique percolation. This approach can be considered as a generalisation of edge percolation with a great potential as a community finding method in real-world graphs. We present a detailed study of the critical point for the appearance of a giant kclique percolation cluster in the Erdős-Rényi-graph. The observed transition is continuous and at the transition point the scaling of the giant component with the number of vertices is highly non-trivial. The concept is extended to weighted and directed graphs as well. Finally, we demonstrate the effectiveness of k-clique percolation as a community finding method via a series of real-world applications.

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Module identification in bipartite and directed networks

Cited by 378 publications

References 49 publications

Detecting evolving patterns of self‐organizing networks by flow hierarchy measurement

Detecting evolving patterns of self‐organizing networks by flow hierarchy measurement

High-Reproducibility and High-Accuracy Method for Automated Topic Classification

k-Clique Percolation and Clustering

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