391 research outputs found
Twice-Ramanujan Sparsifiers
We prove that every graph has a spectral sparsifier with a number of edges
linear in its number of vertices. As linear-sized spectral sparsifiers of
complete graphs are expanders, our sparsifiers of arbitrary graphs can be
viewed as generalizations of expander graphs.
In particular, we prove that for every and every undirected, weighted
graph on vertices, there exists a weighted graph
with at most \ceil{d(n-1)} edges such that for every , where and
are the Laplacian matrices of and , respectively. Thus,
approximates spectrally at least as well as a Ramanujan expander with
edges approximates the complete graph. We give an elementary
deterministic polynomial time algorithm for constructing
Deterministic Approximation of Random Walks in Small Space
We give a deterministic, nearly logarithmic-space algorithm that given an undirected graph G, a positive integer r, and a set S of vertices, approximates the conductance of S in the r-step random walk on G to within a factor of 1+epsilon, where epsilon>0 is an arbitrarily small constant. More generally, our algorithm computes an epsilon-spectral approximation to the normalized Laplacian of the r-step walk.
Our algorithm combines the derandomized square graph operation [Eyal Rozenman and Salil Vadhan, 2005], which we recently used for solving Laplacian systems in nearly logarithmic space [Murtagh et al., 2017], with ideas from [Cheng et al., 2015], which gave an algorithm that is time-efficient (while ours is space-efficient) and randomized (while ours is deterministic) for the case of even r (while ours works for all r). Along the way, we provide some new results that generalize technical machinery and yield improvements over previous work. First, we obtain a nearly linear-time randomized algorithm for computing a spectral approximation to the normalized Laplacian for odd r. Second, we define and analyze a generalization of the derandomized square for irregular graphs and for sparsifying the product of two distinct graphs. As part of this generalization, we also give a strongly explicit construction of expander graphs of every size
Large Low-Diameter Graphs are Good Expanders
We revisit the classical question of the relationship between the diameter of a graph and its expansion properties. One direction is well understood: expander graphs exhibit essentially the lowest possible diameter. We focus on the reverse direction, showing that "sufficiently large" graphs of fixed diameter and degree must be "good" expanders. We prove this statement for various definitions of "sufficiently large" (multiplicative/additive factor from the largest possible size), for different forms of expansion (edge, vertex, and spectral expansion), and for both directed and undirected graphs. A recurring theme is that the lower the diameter of the graph and (more importantly) the larger its size, the better the expansion guarantees. Aside from inherent theoretical interest, our motivation stems from the domain of network design. Both low-diameter networks and expanders are prominent approaches to designing high-performance networks in parallel computing, HPC, datacenter networking, and beyond. Our results establish that these two approaches are, in fact, inextricably intertwined. We leave the reader with many intriguing questions for future research
Topology of random simplicial complexes: a survey
This expository article is based on a lecture from the Stanford Symposium on
Algebraic Topology: Application and New Directions, held in honor of Gunnar
Carlsson, Ralph Cohen, and Ib Madsen.Comment: After revisions, now 21 pages, 5 figure
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