183 research outputs found
The power of vertex sparsifiers in dynamic graph algorithms
We introduce a new algorithmic framework for designing dynamic graph algorithms in minor-free graphs, by exploiting the structure of such graphs and a tool called vertex sparsification, which is a way to compress large graphs into small ones that well preserve relevant properties among a subset of vertices and has previously mainly been used in the design of approximation algorithms. Using this framework, we obtain a Monte Carlo randomized fully dynamic algorithm for (1 + epsilon)-approximating the energy of electrical flows in n-vertex planar graphs with tilde{O}(r epsilon^{-2}) worst-case update time and tilde{O}((r + n / sqrt{r}) epsilon^{-2}) worst-case query time, for any r larger than some constant. For r=n^{2/3}, this gives tilde{O}(n^{2/3} epsilon^{-2}) update time and tilde{O}(n^{2/3} epsilon^{-2}) query time. We also extend this algorithm to work for minor-free graphs with similar approximation and running time guarantees. Furthermore, we illustrate our framework on the all-pairs max flow and shortest path problems by giving corresponding dynamic algorithms in minor-free graphs with both sublinear update and query times. To the best of our knowledge, our results are the first to systematically establish such a connection between dynamic graph algorithms and vertex sparsification. We also present both upper bound and lower bound for maintaining the energy of electrical flows in the incremental subgraph model, where updates consist of only vertex activations, which might be of independent interest
Expander Decomposition in Dynamic Streams
In this paper we initiate the study of expander decompositions of a graph
in the streaming model of computation. The goal is to find a
partitioning of vertices such that the subgraphs of
induced by the clusters are good expanders, while the
number of intercluster edges is small. Expander decompositions are classically
constructed by a recursively applying balanced sparse cuts to the input graph.
In this paper we give the first implementation of such a recursive sparsest cut
process using small space in the dynamic streaming model.
Our main algorithmic tool is a new type of cut sparsifier that we refer to as
a power cut sparsifier - it preserves cuts in any given vertex induced subgraph
(or, any cluster in a fixed partition of ) to within a -multiplicative/additive error with high probability. The power cut
sparsifier uses space and edges, which we show is
asymptotically tight up to polylogarithmic factors in for constant
.Comment: 31 pages, 0 figures, to appear in ITCS 202
Sketching Cuts in Graphs and Hypergraphs
Sketching and streaming algorithms are in the forefront of current research
directions for cut problems in graphs. In the streaming model, we show that
-approximation for Max-Cut must use space;
moreover, beating -approximation requires polynomial space. For the
sketching model, we show that -uniform hypergraphs admit a
-cut-sparsifier (i.e., a weighted subhypergraph that
approximately preserves all the cuts) with
edges. We also make first steps towards sketching general CSPs (Constraint
Satisfaction Problems)
Fully Dynamic Effective Resistances
In this paper we consider the \emph{fully-dynamic} All-Pairs Effective
Resistance problem, where the goal is to maintain effective resistances on a
graph among any pair of query vertices under an intermixed sequence of edge
insertions and deletions in . The effective resistance between a pair of
vertices is a physics-motivated quantity that encapsulates both the congestion
and the dilation of a flow. It is directly related to random walks, and it has
been instrumental in the recent works for designing fast algorithms for
combinatorial optimization problems, graph sparsification, and network science.
We give a data-structure that maintains -approximations to
all-pair effective resistances of a fully-dynamic unweighted, undirected
multi-graph with expected amortized
update and query time, against an oblivious adversary. Key to our result is the
maintenance of a dynamic \emph{Schur complement}~(also known as vertex
resistance sparsifier) onto a set of terminal vertices of our choice.
This maintenance is obtained (1) by interpreting the Schur complement as a
sum of random walks and (2) by randomly picking the vertex subset into which
the sparsifier is constructed. We can then show that each update in the graph
affects a small number of such walks, which in turn leads to our sub-linear
update time. We believe that this local representation of vertex sparsifiers
may be of independent interest
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