28,848 research outputs found
A metric on directed graphs and Markov chains based on hitting probabilities
The shortest-path, commute time, and diffusion distances on undirected graphs
have been widely employed in applications such as dimensionality reduction,
link prediction, and trip planning. Increasingly, there is interest in using
asymmetric structure of data derived from Markov chains and directed graphs,
but few metrics are specifically adapted to this task. We introduce a metric on
the state space of any ergodic, finite-state, time-homogeneous Markov chain
and, in particular, on any Markov chain derived from a directed graph. Our
construction is based on hitting probabilities, with nearness in the metric
space related to the transfer of random walkers from one node to another at
stationarity. Notably, our metric is insensitive to shortest and average walk
distances, thus giving new information compared to existing metrics. We use
possible degeneracies in the metric to develop an interesting structural theory
of directed graphs and explore a related quotienting procedure. Our metric can
be computed in time, where is the number of states, and in
examples we scale up to nodes and edges on a desktop
computer. In several examples, we explore the nature of the metric, compare it
to alternative methods, and demonstrate its utility for weak recovery of
community structure in dense graphs, visualization, structure recovering,
dynamics exploration, and multiscale cluster detection.Comment: 26 pages, 9 figures, for associated code, visit
https://github.com/zboyd2/hitting_probabilities_metric, accepted at SIAM J.
Math. Data Sc
Combinatorial modulus and type of graphs
Let a be the 1-skeleton of a triangulated topological annulus. We
establish bounds on the combinatorial modulus of a refinement , formed by
attaching new vertices and edges to , that depend only on the refinement and
not on the structure of itself. This immediately applies to showing that a
disk triangulation graph may be refined without changing its combinatorial
type, provided the refinement is not too wild. We also explore the type problem
in terms of disk growth, proving a parabolicity condition based on a
superlinear growth rate, which we also prove optimal. We prove our results with
no degree restrictions in both the EEL and VEL settings and examine type
problems for more general complexes and dual graphs.Comment: 24 pages, 12 figure
Centrality metrics and localization in core-periphery networks
Two concepts of centrality have been defined in complex networks. The first
considers the centrality of a node and many different metrics for it has been
defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality,
etc). The second is related to a large scale organization of the network, the
core-periphery structure, composed by a dense core plus an outlying and
loosely-connected periphery. In this paper we investigate the relation between
these two concepts. We consider networks generated via the Stochastic Block
Model, or its degree corrected version, with a strong core-periphery structure
and we investigate the centrality properties of the core nodes and the ability
of several centrality metrics to identify them. We find that the three measures
with the best performance are marginals obtained with belief propagation,
PageRank, and degree centrality, while non-backtracking and eigenvector
centrality (or MINRES}, showed to be equivalent to the latter in the large
network limit) perform worse in the investigated networks.Comment: 15 pages, 8 figure
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