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Register a new userA Local Clustering Algorithm for Massive Graphs and its Application to Nearly-Linear Time Graph Partitioning
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Daniel A. Spielman
Publication date
2008
fields
Informatics Engineering
and research's language is
English
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We study the design of local algorithms for massive graphs. A local algorithm is one that finds a solution containing or near a given vertex without looking at the whole graph. We present a local clustering algorithm. Our algorithm finds a good cluster--a subset of vertices whose internal connections are significantly richer than its external connections--near a given vertex. The running time of our algorithm, when it finds a non-empty local cluster, is nearly linear in the size of the cluster it outputs. Our clustering algorithm could be a useful primitive for handling massive graphs, such as social networks and web-graphs. As an application of this clustering algorithm, we present a partitioning algorithm that finds an approximate sparsest cut with nearly optimal balance. Our algorithm takes time nearly linear in the number edges of the graph. Using the partitioning algorithm of this paper, we have designed a nearly-linear time algorithm for constructing spectral sparsifiers of graphs, which we in turn use in a nearly-linear time algorithm for solving linear systems in symmetric, diagonally-dominant matrices. The linear system solver also leads to a nearly linear-time algorithm for approximating the second-smallest eigenvalue and corresponding eigenvector of the Laplacian matrix of a graph. These other results are presented in two companion papers.
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We consider the problem of maximizing a monotone submodular function subject to a knapsack constraint. Our main contribution is an algorithm that achieves a nearly-optimal, $1 - 1/e - epsilon$ approximation, using $(1/epsilon)^{O(1/epsilon^4)} n log^2{n}$ function evaluations and arithmetic operations. Our algorithm is impractical but theoretically interesting, since it overcomes a fundamental running time bottleneck of the multilinear extension relaxation framework. This is the main approach for obtaining nearly-optimal approximation guarantees for important classes of constraints but it leads to $Omega(n^2)$ running times, since evaluating the multilinear extension is expensive. Our algorithm maintains a fractional solution with only a constant number of entries that are strictly fractional, which allows us to overcome this obstacle.
Let $G = (V,E,w)$ be a weighted undirected graph on $|V| = n$ vertices and $|E| = m$ edges, let $k ge 1$ be any integer, and let $epsilon < 1$ be any parameter. We present the following results on fast constructions of spanners with near-optimal sparsity and lightness, which culminate a long line of work in this area. (By near-optimal we mean optimal under ErdH{o}s girth conjecture and disregarding the $epsilon$-dependencies.) - There are (deterministic) algorithms for constructing $(2k-1)(1+epsilon)$-spanners for $G$ with a near-optimal sparsity of $O(n^{1/k} log(1/epsilon)/epsilon))$. The first algorithm can be implemented in the pointer-machine model within time $O(malpha(m,n) log(1/epsilon)/epsilon) + SORT(m))$, where $alpha( , )$ is the two-parameter inverse-Ackermann function and $SORT(m)$ is the time needed to sort $m$ integers. The second algorithm can be implemented in the WORD RAM model within time $O(m log(1/epsilon)/epsilon))$. - There is a (deterministic) algorithm for constructing a $(2k-1)(1+epsilon)$-spanner for $G$ that achieves a near-optimal bound of $O(n^{1/k}mathrm{poly}(1/epsilon))$ on both sparsity and lightness. This algorithm can be implemented in the pointer-machine model within time $O(malpha(m,n) mathrm{poly}(1/epsilon) + SORT(m))$ and in the WORD RAM model within time $O(m alpha(m,n) mathrm{poly}(1/epsilon))$. The previous fastest constructions of $(2k-1)(1+epsilon)$-spanners with near-optimal sparsity incur a runtime of is $O(min{m(n^{1+1/k}) + nlog n,k n^{2+1/k}})$, even regardless of the lightness. Importantly, the greedy spanner for stretch $2k-1$ has sparsity $O(n^{1/k})$ -- with no $epsilon$-dependence whatsoever, but its runtime is $O(m(n^{1+1/k} + nlog n))$. Moreover, the state-of-the-art lightness bound of any $(2k-1)$-spanner is poor, even regardless of the sparsity and runtime.
A graph $G = (V,E)$ is a double-threshold graph if there exist a vertex-weight function $w colon V to mathbb{R}$ and two real numbers $mathtt{lb}, mathtt{ub} in mathbb{R}$ such that $uv in E$ if and only if $mathtt{lb} le mathtt{w}(u) + mathtt{w}(v) le mathtt{ub}$. In the literature, those graphs are studied as the pairwise compatibility graphs that have stars as their underlying trees. We give a new characterization of double-threshold graphs, which gives connections to bipartite permutation graphs. Using the new characterization, we present a linear-time algorithm for recognizing double-threshold graphs. Prior to our work, the fastest known algorithm by Xiao and Nagamochi [COCOON 2018] ran in $O(n^6)$ time, where $n$ is the number of vertices.
The graph isomorphism problem is of practical importance, as well as being a theoretical curiosity in computational complexity theory in that it is not known whether it is $NP$-complete or $P$. However, for many graphs, the problem is tractable, and related to the problem of finding the automorphism group of the graph. Perhaps the most well known state-of-the art implementation for finding the automorphism group is Nauty. However, Nauty is particularly susceptible to poor performance on star configurations, where the spokes of the star are isomorphic with each other. In this work, I present an algorithm that explodes these star configurations, reducing the problem to a sequence of simpler automorphism group calculations.
We present an $tilde O(m+n^{1.5})$-time randomized algorithm for maximum cardinality bipartite matching and related problems (e.g. transshipment, negative-weight shortest paths, and optimal transport) on $m$-edge, $n$-node graphs. For maximum cardinality bipartite matching on moderately dense graphs, i.e. $m = Omega(n^{1.5})$, our algorithm runs in time nearly linear in the input size and constitutes the first improvement over the classic $O(msqrt{n})$-time [Dinic 1970; Hopcroft-Karp 1971; Karzanov 1973] and $tilde O(n^omega)$-time algorithms [Ibarra-Moran 1981] (where currently $omegaapprox 2.373$). On sparser graphs, i.e. when $m = n^{9/8 + delta}$ for any constant $delta>0$, our result improves upon the recent advances of [Madry 2013] and [Liu-Sidford 2020b, 2020a] which achieve an $tilde O(m^{4/3+o(1)})$ runtime. We obtain these results by combining and advancing recent lines of research in interior point methods (IPMs) and dynamic graph algorithms. First, we simplify and improve the IPM of [v.d.Brand-Lee-Sidford-Song 2020], providing a general primal-dual IPM framework and new sampling-based techniques for handling infeasibility induced by approximate linear system solvers. Second, we provide a simple sublinear-time algorithm for detecting and sampling high-energy edges in electric flows on expanders and show that when combined with recent advances in dynamic expander decompositions, this yields efficient data structures for maintaining the iterates of both [v.d.Brand et al.] and our new IPMs. Combining this general machinery yields a simpler $tilde O(n sqrt{m})$ time algorithm for matching based on the logarithmic barrier function, and our state-of-the-art $tilde O(m+n^{1.5})$ time algorithm for matching based on the [Lee-Sidford 2014] barrier (as regularized in [v.d.Brand et al.]).
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