T
Tamás Nepusz
Researcher at Eötvös Loránd University
Publications - 58
Citations - 14778
Tamás Nepusz is an academic researcher from Eötvös Loránd University. The author has contributed to research in topics: Flocking (behavior) & Food safety. The author has an hindex of 28, co-authored 57 publications receiving 12326 citations. Previous affiliations of Tamás Nepusz include Kingston University & University of London.
Papers
More filters
The igraph software package for complex network research
Gábor Csárdi,Tamás Nepusz +1 more
TL;DR: Platform-independent and open source igraph aims to satisfy all the requirements of a graph package while possibly remaining easy to use in interactive mode as well.
Journal ArticleDOI
Detecting overlapping protein complexes in protein-protein interaction networks
TL;DR: ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence catalog and complexes derived from the Saccharomyces Genome Database than the results of seven popular methods.
Journal ArticleDOI
A census of human soluble protein complexes.
Pierre C. Havugimana,G. Traver Hart,Tamás Nepusz,Haixuan Yang,Andrei L. Turinsky,Zhihua Li,Peggy I. Wang,Daniel R. Boutz,Vincent Fong,Sadhna Phanse,Mohan Babu,Stephanie A. Craig,Pingzhao Hu,Cuihong Wan,James Vlasblom,Vaqaar Un Nisa Dar,Alexandr Bezginov,Greg W. Clark,Gabriel C. Wu,Shoshana J. Wodak,Elisabeth R. M. Tillier,Alberto Paccanaro,Edward M. Marcotte,Andrew Emili +23 more
TL;DR: Whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.
Journal ArticleDOI
Controlling edge dynamics in complex networks
TL;DR: A study of the controllability of network edge dynamics reveals that it differs from that of nodal dynamics, and that real-world networks are easier to control than their random counterparts.
Journal ArticleDOI
Fuzzy communities and the concept of bridgeness in complex networks.
TL;DR: An algorithm for determining the optimal membership degrees with respect to a given goal function is created, and a measure is introduced that is able to identify outlier vertices that do not belong to any of the communities, bridges that have significant membership in more than one single community, and regular Vertices that fundamentally restrict their interactions within their own community.