465 research outputs found
Using network centrality measures to manage landscape connectivity
We use a graph-theoretical landscape modeling approach to investigate how to identify central patches in the landscape as well as how these central patches influence (1) organism movement within the local neighborhood, and (2) the dispersal of organisms beyond the local neighborhood. Organism movements were theoretically estimated based on the spatial configuration of the habitat patches in the studied landscape. We find that centrality depends on the way the graph-theoretical model of habitat patches is constructed, although even the simplest network representation, not taking strength and directionality of potential organisms flows into account, still provides a coarse-grained assessment of the most important patches according to their contribution to landscape connectivity. Moreover, we identify (at least) two general classes of centrality. One accounts for the local flow of organisms in the neighborhood of a patch and the other for the ability to maintain connectivity beyond the scale of the local neighborhood. Finally, we study how habitat patches with high scores on different network centrality measures are distributed in a fragmented agricultural landscape in Madagascar. Results show that patches with high degree-, and betweenness centrality are widely spread, while patches with high subgraph- and closeness centrality are clumped together in dense clusters. This finding may enable multi-species analyses of single-species network models
The Network Analysis of Urban Streets: A Primal Approach
The network metaphor in the analysis of urban and territorial cases has a
long tradition especially in transportation/land-use planning and economic
geography. More recently, urban design has brought its contribution by means of
the "space syntax" methodology. All these approaches, though under different
terms like accessibility, proximity, integration,connectivity, cost or effort,
focus on the idea that some places (or streets) are more important than others
because they are more central. The study of centrality in complex
systems,however, originated in other scientific areas, namely in structural
sociology, well before its use in urban studies; moreover, as a structural
property of the system, centrality has never been extensively investigated
metrically in geographic networks as it has been topologically in a wide range
of other relational networks like social, biological or technological. After
two previous works on some structural properties of the dual and primal graph
representations of urban street networks (Porta et al. cond-mat/0411241;
Crucitti et al. physics/0504163), in this paper we provide an in-depth
investigation of centrality in the primal approach as compared to the dual one,
with a special focus on potentials for urban design.Comment: 19 page, 4 figures. Paper related to the paper "The Network Analysis
of Urban Streets: A Dual Approach" cond-mat/041124
Recurrence networks - A novel paradigm for nonlinear time series analysis
This paper presents a new approach for analysing structural properties of
time series from complex systems. Starting from the concept of recurrences in
phase space, the recurrence matrix of a time series is interpreted as the
adjacency matrix of an associated complex network which links different points
in time if the evolution of the considered states is very similar. A critical
comparison of these recurrence networks with similar existing techniques is
presented, revealing strong conceptual benefits of the new approach which can
be considered as a unifying framework for transforming time series into complex
networks that also includes other methods as special cases.
It is demonstrated that there are fundamental relationships between the
topological properties of recurrence networks and the statistical properties of
the phase space density of the underlying dynamical system. Hence, the network
description yields new quantitative characteristics of the dynamical complexity
of a time series, which substantially complement existing measures of
recurrence quantification analysis
Axiomatic Characterization of PageRank
This paper examines the fundamental problem of identifying the most important
nodes in a network. We use an axiomatic approach to this problem. Specifically,
we propose six simple properties and prove that PageRank is the only centrality
measure that satisfies all of them. Our work gives new conceptual and
theoretical foundations of PageRank that can be used to determine suitability
of this centrality measure in specific applications
How to choose the most appropriate centrality measure?
We propose a new method to select the most appropriate network centrality
measure based on the user's opinion on how such a measure should work on a set
of simple graphs. The method consists in: (1) forming a set of
candidate measures; (2) generating a sequence of sufficiently simple graphs
that distinguish all measures in on some pairs of nodes; (3) compiling
a survey with questions on comparing the centrality of test nodes; (4)
completing this survey, which provides a centrality measure consistent with all
user responses. The developed algorithms make it possible to implement this
approach for any finite set of measures. This paper presents its
realization for a set of 40 centrality measures. The proposed method called
culling can be used for rapid analysis or combined with a normative approach by
compiling a survey on the subset of measures that satisfy certain normative
conditions (axioms). In the present study, the latter was done for the subsets
determined by the Self-consistency or Bridge axioms.Comment: 26 pages, 1 table, 1 algorithm, 8 figure
Homothetic Behavior of Betweenness Centralities: A Multiscale Alternative Approach to Relate Cities and Large Regional Structures
Regional configuration can reveal important aspects about city sustainability, as local-regional interactions shape the evolution and inner geography of urban settlements. However, modelling these large-scale structures remains a challenge, due to their sheer size as physical objects. Despite recent improvements in processing power and computing methods, extensive time periods are still required for ordinary microprocessors to model network centralities in road-graphs with high element counts, connectivity and topological depth. Generalization is often the chosen option to mitigate time-constraints of regional network complexity. Nevertheless, this can impact visual representation and model precision, especially when multiscale comparisons are desired. Tests using Normalized Angular Choice (NACH), a Space Syntax mathematical derivative of Betweenness Centrality, found recursive visual similitudes in centrality spatial distribution when modelling distinct scaled map sections of the same large regional network structure. Therefore, a sort of homothetic behavior is identified, since statistical analyses demonstrate that centrality values and distributions remain rather consistent throughout scales, even when considering edge effects. This paper summarizes these results and considers homotheties as an alternative to extensive network generalization. Hence, data maps can be constructed sooner and more accurately as âpieces of a puzzleâ, since each individual lesser scale graph possesses a faster processing time
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