Powerlaw: a Python package for analysis of heavy-tailed distributions.
about
Choosing experiments to accelerate collective discoveryCan Invalid Bioactives Undermine Natural Product-Based Drug Discovery?A quantitative assessment of Arctic shipping in 2010-2014.Quantifying long-term evolution of intra-urban spatial interactions.Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving MelanomaQuantifying the Economic and Cultural Biases of Social Media through Trending TopicsHIV competition dynamics over sexual networks: first comer advantage conserves founder effects.Tracing the Attention of Moving Citizens.Spontaneous cortical activity is transiently poised close to criticalityQuantiprot - a Python package for quantitative analysis of protein sequences.Homophily and the speed of social mobilization: the effect of acquired and ascribed traits.Discovering functional modules across diverse maize transcriptomes using COB, the Co-expression Browser.Collective philanthropy: describing and modeling the ecology of giving.Emergent user behavior on Twitter modelled by a stochastic differential equationA Complex Network Approach to Distributional Semantic Models.Discriminating between Light- and Heavy-Tailed Distributions with Limit Theorem.Functional Analysis and Characterization of Differential Coexpression Networks.How to Study the City on Instagram.A detailed heterogeneous agent model for a single asset financial market with trading via an order book.Criticality meets learning: Criticality signatures in a self-organizing recurrent neural networkTopological constraints on network control profiles.Functional network inference of the suprachiasmatic nucleus.Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC ToolboxCriticality Maximizes Complexity in Neural Tissue.Exploring the topological sources of robustness against invasion in biological and technological networksSimple molecules as complex systems.Rich cell-type-specific network topology in neocortical microcircuitry.Statistical fluctuations in pedestrian evacuation times and the effect of social contagion.Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins.Measuring political polarization: Twitter shows the two sides of Venezuela.Understanding and controlling regime switching in molecular diffusion.Emergence of encounter networks due to human mobility.Explaining the power-law distribution of human mobility through transportation modality decomposition.Aggregated responses of human mobility to severe winter storms: An empirical study.Structure of the human chromosome interaction network.Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks.Extremes of fractional noises: A model for the timings of arrhythmic heart beats in post-infarction patients.Avalanches, plasticity, and ordering in colloidal crystals under compression.High-veracity functional imaging in scanning probe microscopy via Graph-Bootstrapping.Evolutionary dynamics of cryptocurrency transaction networks: An empirical study
P2860
Q24289251-51A8BAE4-0A5C-4BB3-9D38-24F2DE39AED2Q26778522-1F11166E-3139-4A62-83E0-1A8D79842CD1Q27321472-309C46DD-5A3B-402D-87A5-EF947A837C96Q27345506-1A518F72-17BA-424D-9353-1DB384203BC1Q28550898-C4F3A0F6-9F77-4A20-9B5B-90B0D3C4EBAFQ28645881-000570B9-1387-4E88-9A37-546A773E5B55Q30371471-073239DD-4C0C-41C5-9E64-424366512D73Q33450636-E4166534-9299-4E4A-8BA1-50EEB4503B59Q33778865-CEA2D62B-5064-491D-8281-C62AD93D40A1Q33909222-4D04F3A0-2F89-41A7-9E89-987D08574BDBQ35150517-5CD5F170-06A0-4726-8044-4CE1C1FE8BCAQ35186754-405A523A-AB86-4D38-8D16-FAC7B3535209Q35199224-C06393AE-39F9-4A96-B357-8C80129939A5Q35625461-F5D8779B-E446-4C82-BBE5-2C5EF1899477Q35752358-00F0BA94-2362-4712-8061-A2311EB58B8AQ35876973-061296C0-1905-4E6C-971D-5BDBC1DDFC26Q35964445-A448861F-D299-4608-8DBC-CB6F0769214EQ36060063-65AD4E5D-1F6A-44A9-9385-2E7B29ACE874Q36292834-77CF639E-5343-4843-AF63-5858FBA4AE48Q36383870-66E72817-ACB6-4E1B-9DED-B3F262DC712AQ36392820-8F0F4F22-1888-4C58-A043-0FD5FFB47E06Q36831463-CCBED1D1-EA8F-4FCB-9ACD-545685DFAE3EQ37039537-17C17C80-8D98-493E-B467-72B7DE193429Q37284870-BE05DBCC-8B04-4AF3-B4E0-2CEC52A6D3AAQ38333471-983C3FD7-EC3A-4509-ABC3-F0A0F2355BB7Q38709118-3726C393-1AD7-4747-9AFF-98F23B1CD23BQ38743899-AC0A4E14-A217-47AE-9EFA-09B04FA206EBQ39391415-F6EBDFD8-19B0-4894-B7FA-8F75C75E818FQ41049584-7106664D-9EE6-4883-8657-4D6672BBECC1Q41125992-EEDE40F8-BD6C-447A-9DC9-1F34DDB626A5Q41536935-7AFF7F77-84E9-456A-A399-F20465FDD73FQ41986618-277B555A-D7D0-481E-855F-83BA623D2581Q42109657-CCA80518-AB2F-4743-B2EA-700053D00CBEQ46875126-DE150D1B-732D-4F97-8FD0-FE50874ACF59Q47120161-2D4E505E-7866-4F1E-A935-F64C82FCA902Q47552195-3E7F0410-3610-4C51-B3CE-339390D29CC0Q47894071-471E1BCE-14DD-47D7-B868-4ECD00B72ACFQ53590764-E0C433B3-C80B-441F-81CD-F9B5E2E9084DQ55263025-6B43EED3-1F9F-4F85-8445-D81C4E453812Q57168844-0C2C7B99-2EE8-4776-AD63-0F9A3A341321
P2860
Powerlaw: a Python package for analysis of heavy-tailed distributions.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 29 January 2014
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Powerlaw: a Python package for analysis of heavy-tailed distributions.
@en
Powerlaw: a Python package for analysis of heavy-tailed distributions.
@nl
type
label
Powerlaw: a Python package for analysis of heavy-tailed distributions.
@en
Powerlaw: a Python package for analysis of heavy-tailed distributions.
@nl
prefLabel
Powerlaw: a Python package for analysis of heavy-tailed distributions.
@en
Powerlaw: a Python package for analysis of heavy-tailed distributions.
@nl
P2860
P1433
P1476
Powerlaw: a Python package for analysis of heavy-tailed distributions
@en
P2093
Dietmar Plenz
Jeff Alstott
P2860
P304
P356
10.1371/JOURNAL.PONE.0085777
P407
P577
2014-01-29T00:00:00Z