21,766 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
Local Ranking Problem on the BrowseGraph
The "Local Ranking Problem" (LRP) is related to the computation of a
centrality-like rank on a local graph, where the scores of the nodes could
significantly differ from the ones computed on the global graph. Previous work
has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a
graph where nodes are webpages and edges are browsing transitions. Recently,
this graph has received more and more attention in many different tasks such as
ranking, prediction and recommendation. However, a web-server has only the
browsing traffic performed on its pages (local BrowseGraph) and, as a
consequence, the local computation can lead to estimation errors, which hinders
the increasing number of applications in the state of the art. Also, although
the divergence between the local and global ranks has been measured, the
possibility of estimating such divergence using only local knowledge has been
mainly overlooked. These aspects are of great interest for online service
providers who want to: (i) gauge their ability to correctly assess the
importance of their resources only based on their local knowledge, and (ii)
take into account real user browsing fluxes that better capture the actual user
interest than the static hyperlink network. We study the LRP problem on a
BrowseGraph from a large news provider, considering as subgraphs the
aggregations of browsing traces of users coming from different domains. We show
that the distance between rankings can be accurately predicted based only on
structural information of the local graph, being able to achieve an average
rank correlation as high as 0.8
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
Distinctiveness Centrality in Social Networks
The determination of node centrality is a fundamental topic in social network
studies. As an addition to established metrics, which identify central nodes
based on their brokerage power, the number and weight of their connections, and
the ability to quickly reach all other nodes, we introduce five new measures of
Distinctiveness Centrality. These new metrics attribute a higher score to nodes
keeping a connection with the network periphery. They penalize links to
highly-connected nodes and serve the identification of social actors with more
distinctive network ties. We discuss some possible applications and properties
of these newly introduced metrics, such as their upper and lower bounds.
Distinctiveness centrality provides a viewpoint of centrality alternative to
that of established metrics
Ranking Spaces for Predicting Human Movement in an Urban Environment
A city can be topologically represented as a connectivity graph, consisting
of nodes representing individual spaces and links if the corresponding spaces
are intersected. It turns out in the space syntax literature that some defined
topological metrics can capture human movement rates in individual spaces. In
other words, the topological metrics are significantly correlated to human
movement rates, and individual spaces can be ranked by the metrics for
predicting human movement. However, this correlation has never been well
justified. In this paper, we study the same issue by applying the weighted
PageRank algorithm to the connectivity graph or space-space topology for
ranking the individual spaces, and find surprisingly that (1) the PageRank
scores are better correlated to human movement rates than the space syntax
metrics, and (2) the underlying space-space topology demonstrates small world
and scale free properties. The findings provide a novel justification as to why
space syntax, or topological analysis in general, can be used to predict human
movement. We further conjecture that this kind of analysis is no more than
predicting a drunkard's walking on a small world and scale free network.
Keywords: Space syntax, topological analysis of networks, small world, scale
free, human movement, and PageRankComment: 11 pages, 5 figures, and 2 tables, English corrections from version 1
to version 2, major changes in the section of introduction from version 2 to
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