Dynamic Streaming Spectral Sparsification in Nearly Linear Time and Space

In this paper we consider the problem of computing spectral approximations to graphs in the single pass dynamic streaming model. We provide a linear sketching based solution that given a stream of edge insertions and deletions to a $n$-node undirected graph, uses $\tilde O(n)$ space, processes each update in $\tilde O(1)$ time, and with high probability recovers a spectral sparsifier in $\tilde O(n)$ time. Prior to our work, state of the art results either used near optimal $\tilde O(n)$ space complexity, but brute-force $\Omega(n^2)$ recovery time [Kapralov et al.'14], or with subquadratic runtime, but polynomially suboptimal space complexity [Ahn et al.'14, Kapralov et al.'19]. Our main technical contribution is a novel method for `bucketing' vertices of the input graph into clusters that allows fast recovery of edges of sufficiently large effective resistance. Our algorithm first buckets vertices of the graph by performing ball-carving using (an approximation to) its effective resistance metric, and then recovers the high effective resistance edges from a sketched version of an electrical flow between vertices in a bucket, taking nearly linear time in the number of vertices overall. This process is performed at different geometric scales to recover a sample of edges with probabilities proportional to effective resistances and obtain an actual sparsifier of the input graph. This work provides both the first efficient $\ell_2$-sparse recovery algorithm for graphs and new primitives for manipulating the effective resistance embedding of a graph, both of which we hope have further applications

Graph Spanners in the Message-Passing Model

Graph spanners are sparse subgraphs which approximately preserve all pairwise shortest-path distances in an input graph. The notion of approximation can be additive, multiplicative, or both, and many variants of this problem have been extensively studied. We study the problem of computing a graph spanner when the edges of the input graph are distributed across two or more sites in an arbitrary, possibly worst-case partition, and the goal is for the sites to minimize the communication used to output a spanner. We assume the message-passing model of communication, for which there is a point-to-point link between all pairs of sites as well as a coordinator who is responsible for producing the output. We stress that the subset of edges that each site has is not related to the network topology, which is fixed to be point-to-point. While this model has been extensively studied for related problems such as graph connectivity, it has not been systematically studied for graph spanners. We present the first tradeoffs for total communication versus the quality of the spanners computed, for two or more sites, as well as for additive and multiplicative notions of distortion. We show separations in the communication complexity when edges are allowed to occur on multiple sites, versus when each edge occurs on at most one site. We obtain nearly tight bounds (up to polylog factors) for the communication of additive $2$-spanners in both the with and without duplication models, multiplicative $(2k-1)$-spanners in the with duplication model, and multiplicative $3$ and $5$-spanners in the without duplication model. Our lower bound for multiplicative $3$-spanners employs biregular bipartite graphs rather than the usual Erd\H{o}s girth conjecture graphs and may be of wider interest.Comment: ITCS 202

Two Player Hidden Pointer Chasing and Multi-Pass Lower Bounds in Turnstile Streams

The authors have withdrawn this paper due to an error in the proof of Lemma 3.4. -------------------------------------------------------------------------------------------- The authors have withdrawn this paper due to an error in the proof of Lemma 3.4z(Assadi, Chen, and Khanna, 2019) define a 4-player hidden-pointer-chasing ($\mathsf{HPC}^4$), and using it, give strong multi-pass lower bounds for graph problems in the streaming model of computation and a lower bound on the query complexity of sub-modular minimization. We present a two-player version ($\mathsf{HPC}^2$) of $\mathsf{HPC}^4$ that has matching communication complexity to $\mathsf{HPC}^4$. Our formulation allows us to lower bound its communication complexity with a simple direct-sum argument. Using this lower bound on the communication complexity of $\mathsf{HPC}^2$, we retain the streaming and query complexity lower bounds by (Assadi, Chen, and Khanna, 2019). Further, by giving reductions from $\mathsf{HPC}^2$, we prove new multi-pass space lower bounds for graph problems in turnstile streams. In particular, we show that any algorithm which computes the exact weight of the maximum weighted matching in an $n$-vertex graph requires $\tilde{O}(n^{2})$ space unless it makes $\omega(\log n)$ passes over the turnstile stream, and that any algorithm which computes the minimum $s\text{-}t$ distance in an $n$-vertex graph requires $n^{2-o(1)}$ space unless it makes $n^{\Omega(1)}$ passes over the turnstile stream. Our reductions can be modified to use $\mathsf{HPC}^4$ as well.Comment: The authors have withdrawn this paper due to an error in the proof of Lemma 3.

Sparsification of Balanced Directed Graphs

Sparsification, where the cut values of an input graph are approximately preserved by a sparse graph (called a cut sparsifier) or a succinct data structure (called a cut sketch), has been an influential tool in graph algorithms. But, this tool is restricted to undirected graphs, because some directed graphs are known to not admit sparsification. Such examples, however, are structurally very dissimilar to undirected graphs in that they exhibit highly unbalanced cuts. This motivates us to ask: can we sparsify a balanced digraph? To make this question concrete, we define balance $\beta$ of a digraph as the maximum ratio of the cut value in the two directions (Ene et al., STOC 2016). We show the following results: For-All Sparsification: If all cut values need to be simultaneously preserved (cf. Bencz\'ur and Karger, STOC 1996), then we show that the size of the sparsifier (or even cut sketch) must scale linearly with $\beta$. The upper bound is a simple extension of sparsification of undirected graphs (formally stated recently in Ikeda and Tanigawa (WAOA 2018)), so our main contribution here is to show a matching lower bound. For-Each Sparsification: If each cut value needs to be individually preserved (Andoni et al., ITCS 2016), then the situation is more interesting. Here, we give a cut sketch whose size scales with $\sqrt{\beta}$, thereby beating the linear lower bound above. We also show that this result is tight by exhibiting a matching lower bound of $\sqrt{\beta}$ on "for-each" cut sketches. Our upper bounds work for general weighted graphs, while the lower bounds even hold for unweighted graphs with no parallel edges

Graph Spanners by Sketching in Dynamic Streams and the Simultaneous Communication Model

Graph sketching is a powerful technique introduced by the seminal work of Ahn, Guha and McGregor'12 on connectivity in dynamic graph streams that has enjoyed considerable attention in the literature since then, and has led to near optimal dynamic streaming algorithms for many fundamental problems such as connectivity, cut and spectral sparsifiers and matchings. Interestingly, however, the sketching and dynamic streaming complexity of approximating the shortest path metric of a graph is still far from well-understood. Besides a direct $k$-pass implementation of classical spanner constructions (recently improved to $\lfloor\frac k2\rfloor+1$-passes by Fernandez, Woodruff and Yasuda'20) the state of the art amounts to a $O(\log k)$-pass algorithm of Ahn, Guha and McGregor'12, and a $2$-pass algorithm of Kapralov and Woodruff'14. In particular, no single pass algorithm is known, and the optimal tradeoff between the number of passes, stretch and space complexity is open. In this paper we introduce several new graph sketching techniques for approximating the shortest path metric of the input graph. We give the first {\em single pass} sketching algorithm for constructing graph spanners: we show how to obtain a $\widetilde{O}(n^{\frac23})$-spanner using $\widetilde{O}(n)$ space, and in general a $\widetilde{O}(n^{\frac23(1-\alpha)})$-spanner using $\widetilde{O}(n^{1+\alpha})$ space for every $\alpha\in [0, 1]$, a tradeoff that we think may be close optimal. We also give new spanner construction algorithms for any number of passes, simultaneously improving upon all prior work on this problem. Finally, we study the simultaneous communication model and propose the first protocols with low per player information

Online Spectral Approximation in Random Order Streams

This paper studies spectral approximation for a positive semidefinite matrix in the online setting. It is known in [Cohen et al. APPROX 2016] that we can construct a spectral approximation of a given $n \times d$ matrix in the online setting if an additive error is allowed. In this paper, we propose an online algorithm that avoids an additive error with the same time and space complexities as the algorithm of Cohen et al., and provides a better upper bound on the approximation size when a given matrix has small rank. In addition, we consider the online random order setting where a row of a given matrix arrives uniformly at random. In this setting, we propose time and space efficient algorithms to find a spectral approximation. Moreover, we reveal that a lower bound on the approximation size in the online random order setting is $\Omega (d \epsilon^{-2} \log n)$, which is larger than the one in the offline setting by an $\mathrm{O}\left( \log n \right)$ factor