9,816 research outputs found
Developments in the theory of randomized shortest paths with a comparison of graph node distances
There have lately been several suggestions for parametrized distances on a
graph that generalize the shortest path distance and the commute time or
resistance distance. The need for developing such distances has risen from the
observation that the above-mentioned common distances in many situations fail
to take into account the global structure of the graph. In this article, we
develop the theory of one family of graph node distances, known as the
randomized shortest path dissimilarity, which has its foundation in statistical
physics. We show that the randomized shortest path dissimilarity can be easily
computed in closed form for all pairs of nodes of a graph. Moreover, we come up
with a new definition of a distance measure that we call the free energy
distance. The free energy distance can be seen as an upgrade of the randomized
shortest path dissimilarity as it defines a metric, in addition to which it
satisfies the graph-geodetic property. The derivation and computation of the
free energy distance are also straightforward. We then make a comparison
between a set of generalized distances that interpolate between the shortest
path distance and the commute time, or resistance distance. This comparison
focuses on the applicability of the distances in graph node clustering and
classification. The comparison, in general, shows that the parametrized
distances perform well in the tasks. In particular, we see that the results
obtained with the free energy distance are among the best in all the
experiments.Comment: 30 pages, 4 figures, 3 table
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
Shortest path embeddings of graphs on surfaces
The classical theorem of F\'{a}ry states that every planar graph can be
represented by an embedding in which every edge is represented by a straight
line segment. We consider generalizations of F\'{a}ry's theorem to surfaces
equipped with Riemannian metrics. In this setting, we require that every edge
is drawn as a shortest path between its two endpoints and we call an embedding
with this property a shortest path embedding. The main question addressed in
this paper is whether given a closed surface S, there exists a Riemannian
metric for which every topologically embeddable graph admits a shortest path
embedding. This question is also motivated by various problems regarding
crossing numbers on surfaces.
We observe that the round metrics on the sphere and the projective plane have
this property. We provide flat metrics on the torus and the Klein bottle which
also have this property.
Then we show that for the unit square flat metric on the Klein bottle there
exists a graph without shortest path embeddings. We show, moreover, that for
large g, there exist graphs G embeddable into the orientable surface of genus
g, such that with large probability a random hyperbolic metric does not admit a
shortest path embedding of G, where the probability measure is proportional to
the Weil-Petersson volume on moduli space.
Finally, we construct a hyperbolic metric on every orientable surface S of
genus g, such that every graph embeddable into S can be embedded so that every
edge is a concatenation of at most O(g) shortest paths.Comment: 22 pages, 11 figures: Version 3 is updated after comments of
reviewer
Shortest path distance in random k-nearest neighbor graphs
Consider a weighted or unweighted k-nearest neighbor graph that has been
built on n data points drawn randomly according to some density p on R^d. We
study the convergence of the shortest path distance in such graphs as the
sample size tends to infinity. We prove that for unweighted kNN graphs, this
distance converges to an unpleasant distance function on the underlying space
whose properties are detrimental to machine learning. We also study the
behavior of the shortest path distance in weighted kNN graphs.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
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