66,233 research outputs found
Community core detection in transportation networks
This work analyses methods for the identification and the stability under
perturbation of a territorial community structure with specific reference to
transportation networks. We considered networks of commuters for a city and an
insular region. In both cases, we have studied the distribution of commuters'
trips (i.e., home-to-work trips and viceversa). The identification and
stability of the communities' cores are linked to the land-use distribution
within the zone system, and therefore their proper definition may be useful to
transport planners.Comment: 8 pages, 13 figure
Community detection in airline networks : an empirical analysis of American vs. Southwest airlines
In this paper, we develop a route-traffic-based method for detecting community structures in airline networks. Our model is both an application and an extension of the Clauset-Newman-Moore (CNM) modularity maximization algorithm, in that we apply the CNM algorithm to large airline networks, and take both route distance and passenger volumes into account. Therefore, the relationships between airports are defined not only based on the topological structure of the network but also by a traffic-driven indicator. To illustrate our model, two case studies are presented: American Airlines and Southwest Airlines. Results show that the model is effective in exploring the characteristics of the network connections, including the detection of the most influential nodes and communities on the formation of different network structures. This information is important from an airline operation pattern perspective to identify the vulnerability of networks
Network communities within and across borders
We investigate the impact of borders on the topology of spatially embedded
networks. Indeed territorial subdivisions and geographical borders
significantly hamper the geographical span of networks thus playing a key role
in the formation of network communities. This is especially important in
scientific and technological policy-making, highlighting the interplay between
pressure for the internationalization to lead towards a global innovation
system and the administrative borders imposed by the national and regional
institutions. In this study we introduce an outreach index to quantify the
impact of borders on the community structure and apply it to the case of the
European and US patent co-inventors networks. We find that (a) the US
connectivity decays as a power of distance, whereas we observe a faster
exponential decay for Europe; (b) European network communities essentially
correspond to nations and contiguous regions while US communities span multiple
states across the whole country without any characteristic geographic scale. We
confirm our findings by means of a set of simulations aimed at exploring the
relationship between different patterns of cross-border community structures
and the outreach index.Comment: Scientific Reports 4, 201
Detection of Core-Periphery Structure in Networks Using Spectral Methods and Geodesic Paths
We introduce several novel and computationally efficient methods for
detecting "core--periphery structure" in networks. Core--periphery structure is
a type of mesoscale structure that includes densely-connected core vertices and
sparsely-connected peripheral vertices. Core vertices tend to be well-connected
both among themselves and to peripheral vertices, which tend not to be
well-connected to other vertices. Our first method, which is based on
transportation in networks, aggregates information from many geodesic paths in
a network and yields a score for each vertex that reflects the likelihood that
a vertex is a core vertex. Our second method is based on a low-rank
approximation of a network's adjacency matrix, which can often be expressed as
a tensor-product matrix. Our third approach uses the bottom eigenvector of the
random-walk Laplacian to infer a coreness score and a classification into core
and peripheral vertices. We also design an objective function to (1) help
classify vertices into core or peripheral vertices and (2) provide a
goodness-of-fit criterion for classifications into core versus peripheral
vertices. To examine the performance of our methods, we apply our algorithms to
both synthetically-generated networks and a variety of networks constructed
from real-world data sets.Comment: This article is part of EJAM's December 2016 special issue on
"Network Analysis and Modelling" (available at
https://www.cambridge.org/core/journals/european-journal-of-applied-mathematics/issue/journal-ejm-volume-27-issue-6/D245C89CABF55DBF573BB412F7651ADB
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