Faster Streaming algorithms for graph spanners

Information Processing Letters, 2008

Algorithmica, 2009

This article reports the results of an extensive experimental analysis of efficient algorithms for computing graph spanners in the data streaming model, where an (α,β)-spanner of a graph G is a subgraph S⊆G such that for each pair of vertices the distance in S is at most α times the distance in G plus β. To the best of our knowledge, this is the first computational study of graph spanner algorithms in a streaming setting. We compare experimentally the randomized algorithms proposed by Baswana (http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0611023) and by Elkin (In: Proceedings of the 34th International Colloquium on Automata, Languages and Programming (ICALP 2007), Wroclaw, Poland, pp. 716–727, 9–13 July 2007) for general stretch factors with the deterministic algorithm presented by Ausiello et al. (In: Proceedings of the 15th Annual European Symposium on Algorithms (ESA 2007), Engineering and Applications Track, Eilat, Israel, 8–10 October 2007. LNCS, vol. 4698, pp. 605–617, 2007), designed for building small stretch spanners. All the algorithms we implemented work in a data streaming model where the input graph is given as a stream of edges in arbitrary order, and all of them need a single pass over the data. Differently from the algorithm in Ausiello et al., the algorithms in Baswana (http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0611023) and Elkin (In: Proceedings of the 34th International Colloquium on Automata, Languages and Programming (ICALP 2007), Wroclaw, Poland, pp. 716–727, 9–13 July 2007) need to know in advance the number of vertices in the graph. The results of our experimental investigation on several input families confirm that all these algorithms are very efficient in practice, finding spanners with stretch and size much smaller than the theoretical bounds and comparable to those obtainable by off-line algorithms. Moreover, our experimental findings confirm that small values of the stretch factor are the case of interest in practice, and that the algorithm by Ausiello et al. tends to produce spanners of better quality than the algorithms by Baswana and Elkin, while still using a comparable amount of time and space resources.

Random Structures and Algorithms, 2007

Let G = (V, E) be an undirected weighted graph on |V | = n vertices, and |E| = m edges. A t-spanner of the graph G, for any t ≥ 1, is a subgraph (V, E S ), E S ⊆ E, such that the distance between any pair of vertices in the subgraph is at most t times the distance between them in the graph G. Computing a t-spanner of minimum size (number of edges) has been a widely studied and well motivated problem in computer science. In this paper we present the first linear time randomized algorithm that computes a t-spanner of a given weighted graph. Moreover, the size of the t-spanner computed essentially matches the worst case lower bound implied by a 43 years old girth conjecture made independently by Erdős [26], Bollobás [19], and Bondy & Simonovits .

ACM Transactions on Algorithms, 2012

Spanner of an undirected graph G = (V, E) is a sub graph which is sparse and yet preserves all-pairs distances approximately. More formally, a spanner with stretch t ∈ N is a subgraph (V, ES), ES ⊆ E such that the distance between any two vertices in the subgraph is at most t times their distance in G.

SIAM Journal on Computing, 2009

We explore problems related to computing graph distances in the data-stream model. The goal is to design algorithms that can process the edges of a graph in an arbitrary order given only a limited amount of working memory. We are motivated by both the practical challenge of processing massive graphs such as the web graph and the desire for a better theoretical understanding of the datastream model. In particular, we are interested in the trade-offs between model parameters such as perdata-item processing time, total space, and the number of passes that may be taken over the stream. These trade-offs are more apparent when considering graph problems than they were in previous streaming work that solved problems of a statistical nature. Our results include the following:

2012

Abstract The current research focus on “big data” problems highlights the scale and complexity of analytics required and the high rate at which data may be changing. In this paper, we present our high performance, scalable and portable software, Spatio-Temporal Interaction Networks and Graphs Extensible Representation (STINGER), that includes a graph data structure that enables these applications.

2009

Abstract Data stream processing has recently received increasing attention as a computational paradigm for dealing with massive data sets. Surprisingly, no algorithm with both sublinear space and passes is known for natural graph problems in classical read-only streaming. Motivated by technological factors of modern storage systems, some authors have recently started to investigate the computational power of less restrictive models where writing streams is allowed.

2018

In contrast to the traditional random access memory computational model where the entire input is available in the working memory, the data stream model only provides sequential access to the input. The data stream model is a natural framework to handle large and dynamic data. In this model, we focus on designing algorithms that use sublinear memory and a small number of passes over the stream. Other desirable properties include fast update time, query time, and post processing time. In this dissertation, we consider different problems in graph theory, combinatorial optimization, and high dimensional data processing. The first part of this dissertation focuses on algorithms for graph theory and combinatorial optimization. We present new results for the problems of finding the densest subgraph, counting the number of triangles, finding max cut with bounded components, and finding the maximum $k$ set coverage. The second part of this dissertation considers problems in high dimensional...

Journal of Graph Algorithms and Applications, 2006

We present fully dynamic algorithms for maintaining 3-and 5-spanners of undirected graphs under a sequence of update operations. For unweighted graphs we maintain a 3-spanner or a 5-spanner under insertions and deletions of edges; on a graph with n vertices each operation is performed in O(∆) amortized time over a sequence of Ω(n) updates, where ∆ is the maximum degree of the original graph. The maintained 3-spanner (resp., 5-spanner) has O(n 3/2 ) edges (resp., O(n 4/3 ) edges), which is known to be optimal. On weighted graphs with d different edge cost values, we maintain a 3-or 5-spanner within the same amortized time bounds over a sequence of Ω(d · n) updates. The maintained 3-spanner (resp., 5-spanner) has O(d · n 3/2 ) edges (resp., O(d · n 4/3 ) edges). The same approach can be extended to graphs with real-valued edge costs in the range [1, C].

Lecture Notes in Computer Science, 2013

We introduce and investigate a new notion of resilience in graph spanners. Let S be a spanner of a graph G. Roughly speaking, we say that a spanner S is resilient if all its point-to-point distances are resilient to edge failures. Namely, whenever any edge in G fails, then as a consequence of this failure all distances do not degrade in S substantially more than in G (i.e., the relative distance increases in S are very close to those in the underlying graph G). In this paper we show that sparse resilient spanners exist, and that they can be computed efficiently. arXiv:1303.1559v3 [cs.DS] 23 Apr 2013 O(n 4 3 ) edges [7]