scholarly journals Network models of massive datasets

2004 ◽  
Vol 1 (1) ◽  
pp. 75-89 ◽  
Author(s):  
Vladimir Boginski ◽  
Sergiy Butenko ◽  
Panos Pardalos
Keyword(s):  
Power Law ◽  
Network Models ◽  
Massive Datasets ◽  
Graph Theoretic ◽  
Global Organization ◽  
Standard Graph

We give a brief overview of the methodology of modeling massive datasets arising in various applications as networks. This approach is often useful for extracting non-trivial information from the datasets by applying standard graph-theoretic techniques. We also point out that graphs representing datasets coming from diverse practical fields have a similar power-law structure, which indicates that the global organization and evolution of massive datasets arising in various spheres of life nowadays follow similar natural principles.

scholarly journals Truncation of Power Law Behavior in “Scale-Free” Network Models due to Information Filtering

2002 ◽  
Vol 88 (13) ◽  
Author(s):  
Stefano Mossa ◽  
Marc Barthélémy ◽  
H. Eugene Stanley ◽  
Luís A. Nunes Amaral
Keyword(s):  
Power Law ◽  
Information Filtering ◽  
Network Models ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals Inefficiencies in network models: A graph-theoretic perspective

2018 ◽  
Vol 131 ◽  
pp. 44-50 ◽  
Author(s):  
Pietro Cenciarelli ◽  
Daniele Gorla ◽  
Ivano Salvo
Keyword(s):  
Network Models ◽  
Graph Theoretic

scholarly journals Snowdrift Game on Topologically Alterable Complex Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Zhe Wang ◽  
Hong Yao ◽  
Jun Du ◽  
Xingzhao Peng ◽  
Chao Ding
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Network Models ◽  
Average Degree ◽  
Clustering Coefficient ◽  
Topological Parameters ◽  
Snowdrift Game ◽  
Cooperation Level ◽  
Optimal Value ◽  
Power Law Exponent

In order to study the influence of network’s structure on cooperation level of repeated snowdrift game, in the frame of two kinds of topologically alterable network models, the relation between the cooperation density and the topological parameters was researched. The results show that the network’s cooperation density is correlated reciprocally with power-law exponent and positively with average clustering coefficient; in other words, the more homogenous and less clustered a network, the lower the network’s cooperation level; and the relation between average degree and cooperation density is nonmonotonic; when the average degree deviates from the optimal value, the cooperation density drops.

scholarly journals Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood

Urban Science ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 28 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Open Source ◽  
Census Tract ◽  
Morphological Characteristics ◽  
Network Models ◽  
Data Repository ◽  
Street Network ◽  
Urban Street ◽  
Graph Theoretic ◽  
Urbanized Area ◽  
Scientific Analysis

OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools.

Interplay of ICP and IXP over the Internet with power-law features

2018 ◽  
Vol 29 (01) ◽  
pp. 1850001
Author(s):  
Zhongyan Fan ◽  
Wallace Kit-Sang Tang
Keyword(s):  
Complex Network ◽  
Power Law ◽  
Homogeneous Model ◽  
Autonomous Systems ◽  
Network Models ◽  
Real Data ◽  
The Internet ◽  
Topological Features ◽  
Multi Level ◽  
Artificial Network

The Internet is the largest artificial network consisting of billions of IP devices, managed by tens of thousands of autonomous systems (ASes). Due to its importance, the Internet has received much attention and its topological features, mainly in AS-level, have been widely explored from the complex network perspective. However, most of the previous studies assume a homogeneous model in which nodes are indistinguishable in nature. It may be good for a general study of topological structure, but unfortunately it fails to reflect the functionality. The Internet ecology is in fact heterogeneous and highly complex. It consists of various elements such as Internet Exchange Points (IXPs), Internet Content Providers (ICPs), and normal Autonomous System (ASes), realizing different roles in the Internet. In this paper, we propose level-structured network models for investigating how ICP performs under the AS-topology with power-law features and how IXP enhances its performance from a complex network perspective. Based on real data, our results reveal that the power-law nature of the Internet facilitates content delivery not only in efficiency but also in path redundancy. Moreover, the proposed multi-level framework is able to clearly illustrate the significant benefits gained by ICP from IXP peerings.

scholarly journals On the Consequences of Power-Law Behavior in Partial Correlation Network Models

2017 ◽  
Author(s):  
Matteo Barigozzi ◽  
Christian T. Brownlees ◽  
Gabor Lugosi
Keyword(s):  
Power Law ◽  
Partial Correlation ◽  
Network Models ◽  
Correlation Network

scholarly journals Modulus on graphs as a generalization of standard graph theoretic quantities

2015 ◽  
Vol 19 (13) ◽  
pp. 298-317 ◽  
Author(s):  
Nathan Albin ◽  
Megan Brunner ◽  
Roberto Perez ◽  
Pietro Poggi-Corradini ◽  
Natalie Wiens
Keyword(s):  
Graph Theoretic ◽  
Standard Graph

scholarly journals Power-law partial correlation network models

2018 ◽  
Vol 12 (2) ◽  
pp. 2905-2929 ◽  
Author(s):  
Matteo Barigozzi ◽  
Christian Brownlees ◽  
Gábor Lugosi
Keyword(s):  
Power Law ◽  
Partial Correlation ◽  
Network Models ◽  
Correlation Network

scholarly journals Global organization of neuronal activity only requires unstructured local connectivity

eLife ◽  
2022 ◽  
Vol 11 ◽  
Author(s):  
David Dahmen ◽  
Moritz Layer ◽  
Lukas Deutz ◽  
Paulina Anna Dąbrowska ◽  
Nicole Voges ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Network Models ◽  
Local Connectivity ◽  
Reach To Grasp ◽  
Electrophysiological Recordings ◽  
Global Organization ◽  
Network States ◽  
Low Dimensional ◽  
Coordination Patterns

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet, this parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

scholarly journals Correlated copying in a hidden network model

2021 ◽  
Author(s):  
Max Falkenberg McGillivray
Keyword(s):  
Social Network ◽  
Network Model ◽  
Power Law ◽  
Degree Distribution ◽  
Network Models ◽  
Unusual Property ◽  
Network Comparison ◽  
Network Heterogeneity ◽  
Social Circle ◽  
Hidden Layer

Abstract We introduce the concept of a hidden network model -- a generative two-layer network in which an observed network evolves according to the structure of an underlying hidden layer. We apply the concept to a simple, analytically tractable model of correlated node copying. In contrast to models where nodes are copied uniformly at random, we consider the case in which the set of copied nodes is biased by the underlying hidden network. In the context of a social network, this copied set may be thought of as an individual's inner social circle, whereas the remaining nodes are part of the wider social circle. Correlated copying results in a stretched-exponential degree distribution, suppressing the power-law tail observed in uniform copying, generates networks with significant clustering, and, in contrast to uniform copying, exhibits the unusual property that the number of cliques of size n grows independently of n. We suggest that hidden network models offer an alternative family of null models for network comparison, and may offer a useful conceptual framework for understanding network heterogeneity.

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