Pajek — Analysis and Visualization of Large Networks

Batagelj, Vladimir; Mrvar, Andrej

doi:10.1007/978-3-642-18638-7_4

Cited by 503 publications

(455 citation statements)

References 18 publications

Supporting

Mentioning

436

Contrasting

Unclassified

Order By: Relevance

“…The entries corresponding to the highest accuracies among these 10 indices are emphasized in black. The six real networks for testing are a protein-protein interaction network (PPI) [16], a co-authorship network of scientists who are themselves publishing on the topic of network science (NS) [50], an electrical power-grid of the western US (Grid) [51], a network of the US political blogs (PB) [52], a router-level Internet collected by Rocketfuel Project (INT) [53], and a network of the US air transportation system (USAir) [54]. Detailed information about these networks can be found in Ref.…”

Section: Local Similarity Indicesmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

View full text Add to dashboard Cite

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

show abstract

Section: Local Similarity Indicesmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

View full text Add to dashboard Cite

show abstract

“…Generic tools already exist to analyze and visualize large-scale networks like Pajek (Batagelj and Mrvar, 2003) or NetworkX (networkx.lanl.gov). Those and many others where mainly used and developed for telecom (GraphViz, www.graphviz.org) and theoretical social sciences (SocSciBot Network, webometrics.wlv.ac.uk/networkhelp).…”

Section: Missing Toolsmentioning

confidence: 99%

MR connectomics: Principles and challenges

Hagmann

Cammoun

Gigandet

et al. 2010

Journal of Neuroscience Methods

256

207

View full text Add to dashboard Cite

a b s t r a c t MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

show abstract

“…The figure is produced using SCA scripts (see Methods) and the Pajek software. 38 ture). This pocket appears to be hydrophobic but lacks a distinct pocket.…”

Section: Resultsmentioning

confidence: 99%

Structure-Based Identification of Small Molecule Binding Sites Using a Free Energy Model

Coleman

Salzberg

Cheng

2006

J. Chem. Inf. Model.

View full text Add to dashboard Cite

We separately have shown that the maximal druglike affinity of a given binding site on a protein can be calculated on the basis of the binding-site structure alone by using a desolvation-based free energy model along with the notion that druglike ligands fall into certain physiochemical property ranges. Here, we present an approach where we reformulate the calculated druggability affinity as an additive free energy to facilitate the searching of whole protein surfaces for druglike binding sites. The highest-scoring patches in many cases represent known ligand-binding sites for druggable targets, but not for difficult targets. This approach differs from other approaches in that it does not simply identify pockets with the greatest volume but instead identifies pockets that are likely to be amenable to druglike small-molecule binding. Combining the method with a functional residue prediction method called SCA (statistical coupling analysis) results in the prediction of potentially druggable allosteric binding sites on p38R kinase.

show abstract

Pajek — Analysis and Visualization of Large Networks

Cited by 503 publications

References 18 publications

Link prediction in complex networks: A survey

Link prediction in complex networks: A survey

MR connectomics: Principles and challenges

Structure-Based Identification of Small Molecule Binding Sites Using a Free Energy Model

Contact Info

Product

Resources

About