34,502 research outputs found
List rankings and on-line list rankings of graphs
A -ranking of a graph is a labeling of its vertices from
such that any nontrivial path whose endpoints have the same
label contains a larger label. The least for which has a -ranking is
the ranking number of , also known as tree-depth. The list ranking number of
is the least such that if each vertex of is assigned a set of
potential labels, then can be ranked by labeling each vertex with a label
from its assigned list. Rankings model a certain parallel processing problem in
manufacturing, while the list ranking version adds scheduling constraints. We
compute the list ranking number of paths, cycles, and trees with many more
leaves than internal vertices. Some of these results follow from stronger
theorems we prove about on-line versions of list ranking, where each vertex
starts with an empty list having some fixed capacity, and potential labels are
presented one by one, at which time they are added to the lists of certain
vertices; the decision of which of these vertices are actually to be ranked
with that label must be made immediately.Comment: 16 pages, 3 figure
Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs
Given a graph where vertices represent alternatives and arcs represent
pairwise comparison data, the statistical ranking problem is to find a
potential function, defined on the vertices, such that the gradient of the
potential function agrees with the pairwise comparisons. Our goal in this paper
is to develop a method for collecting data for which the least squares
estimator for the ranking problem has maximal Fisher information. Our approach,
based on experimental design, is to view data collection as a bi-level
optimization problem where the inner problem is the ranking problem and the
outer problem is to identify data which maximizes the informativeness of the
ranking. Under certain assumptions, the data collection problem decouples,
reducing to a problem of finding multigraphs with large algebraic connectivity.
This reduction of the data collection problem to graph-theoretic questions is
one of the primary contributions of this work. As an application, we study the
Yahoo! Movie user rating dataset and demonstrate that the addition of a small
number of well-chosen pairwise comparisons can significantly increase the
Fisher informativeness of the ranking. As another application, we study the
2011-12 NCAA football schedule and propose schedules with the same number of
games which are significantly more informative. Using spectral clustering
methods to identify highly-connected communities within the division, we argue
that the NCAA could improve its notoriously poor rankings by simply scheduling
more out-of-conference games.Comment: 31 pages, 10 figures, 3 table
On the limiting behavior of parameter-dependent network centrality measures
We consider a broad class of walk-based, parameterized node centrality
measures for network analysis. These measures are expressed in terms of
functions of the adjacency matrix and generalize various well-known centrality
indices, including Katz and subgraph centrality. We show that the parameter can
be "tuned" to interpolate between degree and eigenvector centrality, which
appear as limiting cases. Our analysis helps explain certain correlations often
observed between the rankings obtained using different centrality measures, and
provides some guidance for the tuning of parameters. We also highlight the
roles played by the spectral gap of the adjacency matrix and by the number of
triangles in the network. Our analysis covers both undirected and directed
networks, including weighted ones. A brief discussion of PageRank is also
given.Comment: First 22 pages are the paper, pages 22-38 are the supplementary
material
Experimental analysis of the accessibility of drawings with few segments
The visual complexity of a graph drawing is defined as the number of
geometric objects needed to represent all its edges. In particular, one object
may represent multiple edges, e.g., one needs only one line segment to draw two
collinear incident edges. We study the question if drawings with few segments
have a better aesthetic appeal and help the user to asses the underlying graph.
We design an experiment that investigates two different graph types (trees and
sparse graphs), three different layout algorithms for trees, and two different
layout algorithms for sparse graphs. We asked the users to give an aesthetic
ranking on the layouts and to perform a furthest-pair or shortest-path task on
the drawings.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
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