18,502 research outputs found
Superexpanders from group actions on compact manifolds
It is known that the expanders arising as increasing sequences of level sets
of warped cones, as introduced by the second-named author, do not coarsely
embed into a Banach space as soon as the corresponding warped cone does not
coarsely embed into this Banach space. Combining this with non-embeddability
results for warped cones by Nowak and Sawicki, which relate the
non-embeddability of a warped cone to a spectral gap property of the underlying
action, we provide new examples of expanders that do not coarsely embed into
any Banach space with nontrivial type. Moreover, we prove that these expanders
are not coarsely equivalent to a Lafforgue expander. In particular, we provide
infinitely many coarsely distinct superexpanders that are not Lafforgue
expanders. In addition, we prove a quasi-isometric rigidity result for warped
cones.Comment: 16 pages, to appear in Geometriae Dedicat
Algorithm and Complexity for a Network Assortativity Measure
We show that finding a graph realization with the minimum Randi\'c index for
a given degree sequence is solvable in polynomial time by formulating the
problem as a minimum weight perfect b-matching problem. However, the
realization found via this reduction is not guaranteed to be connected.
Approximating the minimum weight b-matching problem subject to a connectivity
constraint is shown to be NP-Hard. For instances in which the optimal solution
to the minimum Randi\'c index problem is not connected, we describe a heuristic
to connect the graph using pairwise edge exchanges that preserves the degree
sequence. In our computational experiments, the heuristic performs well and the
Randi\'c index of the realization after our heuristic is within 3% of the
unconstrained optimal value on average. Although we focus on minimizing the
Randi\'c index, our results extend to maximizing the Randi\'c index as well.
Applications of the Randi\'c index to synchronization of neuronal networks
controlling respiration in mammals and to normalizing cortical thickness
networks in diagnosing individuals with dementia are provided.Comment: Added additional section on application
Random local algorithms
Consider the problem when we want to construct some structure on a bounded
degree graph, e.g. an almost maximum matching, and we want to decide about each
edge depending only on its constant radius neighbourhood. We show that the
information about the local statistics of the graph does not help here. Namely,
if there exists a random local algorithm which can use any local statistics
about the graph, and produces an almost optimal structure, then the same can be
achieved by a random local algorithm using no statistics.Comment: 9 page
Bootstrap percolation in directed and inhomogeneous random graphs
Bootstrap percolation is a process that is used to model the spread of an
infection on a given graph. In the model considered here each vertex is
equipped with an individual threshold. As soon as the number of infected
neighbors exceeds that threshold, the vertex gets infected as well and remains
so forever. We perform a thorough analysis of bootstrap percolation on a novel
model of directed and inhomogeneous random graphs, where the distribution of
the edges is specified by assigning two distinct weights to each vertex,
describing the tendency of it to receive edges from or to send edges to other
vertices. Under the assumption that the limiting degree distribution of the
graph is integrable we determine the typical fraction of infected vertices. Our
model allows us to study a variety of settings, in particular the prominent
case in which the degree distribution has an unbounded variance. Among other
results, we quantify the notion of "systemic risk", that is, to what extent
local adverse shocks can propagate to large parts of the graph through a
cascade, and discover novel features that make graphs prone/resilient to
initially small infections
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