Regularities and dynamics in bisimulation reductions of big graphs

Luo, Yongming; Fletcher, George H. L.; Hidders, Jan; Bra, Paul De; Wu, Yuqing

doi:10.1145/2484425.2484438

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Our S variation's per-iteration time to generate Twitter's FWBW structural summary is faster than the FW summary per-iteration time of [16,17], whose dataset has 500M fewer edges than ours. Furthermore, their system focuses on optimizing the skew of edges across the few large blocks, while we focus on optimizing the many small block at the opposite spectrum of block sizes -where singletons are very common.…”

Section: Hadoop Experimental Setupmentioning

confidence: 80%

Constructing Bisimulation Summaries on a Multi-Core Graph Processing Framework

Khatchadourian

Consens

2015

Proceedings of the GRADES'15

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Bisimulation summaries of graph data have multiple applications, including facilitating graph exploration and enabling query optimization techniques, but efficient, scalable, summary construction is challenging. The literature describes parallel construction algorithms using message-passing, and these have been recently adapted to MapReduce environments. The fixpoint nature of bisimulation is well suited to iterative graph processing, but the existing MapReduce solutions do not drastically decrease per-iteration times as the computation progresses.In this paper, we focus on leveraging parallel multi-core graph frameworks with the goal of constructing summaries in roughly the same amount of time that it takes to input the data into the framework (for a range of real world data graphs) and output the summary. To achieve our goal we introduce a singleton optimization that significantly reduces per-iteration times after only a few iterations. We present experimental results validating that our scalable GraphChi implementation achieves our goal with bisimulation summaries of million to billion edge graphs.

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Section: Hadoop Experimental Setupmentioning

confidence: 80%

Constructing Bisimulation Summaries on a Multi-Core Graph Processing Framework

Khatchadourian

Consens

2015

Proceedings of the GRADES'15

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“…The basic idea is to partition the paths P ≤k in a graph G P ≤1 P ≤2 − P ≤1 into disjoint blocks such that the paths within each block are indistinguishable with respect to queries (i.e., for each block, for every query q, either all paths or no paths of the block appear in q G ). In this way G is "compressed" in the sense that the number of partition blocks is typically orders of magnitude smaller than the number of paths [32]. The structural index is built over these blocks to process queries in two stages.…”

Section: Overall Ideamentioning

confidence: 99%

Language-aware Indexing for Conjunctive Path Queries

Sasaki¹,

Fletcher²,

Onizuka³

2020

Preprint

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Structural indexing is an approach to accelerating query evaluation, whereby data objects are partitioned and indexed reflecting the precise expressive power of a given query language. Each partition block of the index holds exactly those objects that are indistinguishable with respect to queries expressible in the language. Structural indexes have proven successful for XML, RDF, and relational data management.In this paper we study structural indexing for conjunctive path queries (CP Q). CP Q forms the core of contemporary graph query languages such as SPARQL, Cypher, PGQL, and G-CORE. CP Q plays the same fundamental role with respect to contemporary graph query languages as the classic conjunctive queries play for SQL. We develop the first practical structural indexes for this important query language. In particular, we propose a structural index based on k-path-bisimulation, tightly coupled to the expressive power of CP Q, and develop algorithms for efficient query processing with our index. Furthermore, we study workloadaware structural indexes to reduce both the construction and space costs according to a given workload. We demonstrate through extensive experiments using real and synthetic graphs that our methods accelerate query processing by up to multiple orders of magnitude over the state-of-theart methods, without increasing index size.

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“…• In practice, algorithms for computing (bi)simulations in the absence of data stop after a few iterations (Luo, Fletcher, Hidders, Wu, & Bra, 2013b;Luo, Fletcher, Hidders, Bra, & Wu, 2013a). This tells us that when nodes in real-world graphs are distinguishable by ML formulas, they are distinguishable by some small formula.…”

Section: Restricting Paths In Bisimulationsmentioning

confidence: 99%

Bisimulations on Data Graphs

Abriola¹,

Barceió²,

Figueira³

et al. 2018

jair

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Bisimulation provides structural conditions to characterize indistinguishability from an external observer between nodes on labeled graphs. It is a fundamental notion used in many areas, such as verification, graph-structured databases, and constraint satisfaction. However, several current applications use graphs where nodes also contain data (the so called "data graphs"), and where observers can test for equality or inequality of data values (e.g., asking the attribute 'name' of a node to be different from that of all its neighbors). The present work constitutes a first investigation of "data aware" bisimulations on data graphs. We study the problem of computing such bisimulations, based on the observational indistinguishability for XPath ---a language that extends modal logics like PDL with tests for data equality--- with and without transitive closure operators. We show that in general the problem is PSpace-complete, but identify several restrictions that yield better complexity bounds (coNP, PTime) by controlling suitable parameters of the problem, namely the amount of non-locality allowed, and the class of models considered (graphs, DAGs, trees). In particular, this analysis yields a hierarchy of tractable fragments.

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Regularities and dynamics in bisimulation reductions of big graphs

Cited by 9 publications

References 30 publications

Constructing Bisimulation Summaries on a Multi-Core Graph Processing Framework

Constructing Bisimulation Summaries on a Multi-Core Graph Processing Framework

Language-aware Indexing for Conjunctive Path Queries

Bisimulations on Data Graphs

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