Reachability querying: an independent permutation labeling approach

Wei, Hao; Yu, Jeffrey Xu; Lu, Can; Jin, Ruoming

doi:10.1007/s00778-017-0468-3

Cited by 48 publications

(45 citation statements)

References 39 publications

(73 reference statements)

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“…TopChain has a linear index construction time and linear index size, which makes the method scalable. TopChain significantly outperforms the state-of-the-art indexes [14], [16], [11], [17], [19], [20], and supports efficient dynamic update. As temporal graphs can be used to model many networks with time-ordered activities, TopChain is a useful tool for querying and analyzing these graphs.…”

Section: Discussionmentioning

confidence: 99%

“…Existing reachability indexes can be categorized into three groups: (1)Transitive Closure, (2)2-Hop Labels, and (3)La-bel+Search. We compare with the state-of-the-art indexes in each category: PWAH8 [16] in (1); TOL [20] in (2); and GRAIL++ [19], Ferrari [14] and IP+ [17] in (3). We obtained the source codes from the authors.…”

Section: A Performance On Reachability Queriesmentioning

confidence: 99%

“…• We evaluated the performance of TopChain on a set of 15 real temporal graphs. Compared with the stateof-the-art reachability indexes [14], [16], [17], [19], [20], TopChain is from a few times to a few orders of magnitude faster in query processing, with a smaller or comparable index size and index construction cost. Compared with TTL [11], TopChain has significantly better indexing performance, is faster in query processing for most of the datasets, is more scalable, and supports efficient update maintenance.…”

Section: Introductionmentioning

confidence: 99%

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Reachability and time-based path queries in temporal graphs

Huang

Cheng

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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A temporal graph is a graph in which vertices communicate with each other at specific time, e.g., A calls B at 11 a.m. and talks for 7 minutes, which is modeled by an edge from A to B with starting time "11 a.m." and duration "7 mins". Temporal graphs can be used to model many networks with timerelated activities, but efficient algorithms for analyzing temporal graphs are severely inadequate. We study fundamental problems such as answering reachability and time-based path queries in a temporal graph, and propose an efficient indexing technique specifically designed for processing these queries in a temporal graph. Our results show that our method is efficient and scalable in both index construction and query processing.

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Section: Discussionmentioning

confidence: 99%

Section: A Performance On Reachability Queriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reachability and time-based path queries in temporal graphs

Huang

Cheng

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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“…The index-based algorithms create labels for the vertices, and such labels contain the reachability information. These algorithms can be divided into Label-only and Label+G methods [19]. The label-only [1,3,4,8,[10][11][12][13]20] methods use the labels of source and destination vertices only to answer reachability.…”

Section: Index-based Approachmentioning

confidence: 99%

Modular Decomposition-Based Graph Compression for Fast Reachability Detection

2019

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Fast reachability detection is one of the key problems in graph applications. Most of the existing works focus on creating an index and answering reachability based on that index. For these approaches, the index construction time and index size can become a concern for large graphs. More recently query-preserving graph compression has been proposed, and searching reachability over the compressed graph has been shown to be able to significantly improve query performance as well as reducing the index size. In this paper, we introduce a multilevel compression scheme for DAGs, which builds on existing compression schemes, but can further reduce the graph size for many real-world graphs. We propose an algorithm to answer reachability queries using the compressed graph. Extensive experiments with four existing state-of-the-art reachability algorithms and 12 real-world datasets demonstrate that our approach outperforms the existing methods. Experiments with synthetic datasets ensure the scalability of this approach. We also provide a discussion on possible compression for k-reachability. 194 S. Anirban et al. 1 3further reduce the size of the graph obtained by the method in [22]. Our compression utilizes a slightly modified concept of module [14], and constructs a modular decomposition tree. We show how to use the decomposition tree to answer reachability queries over the original graph efficiently. Furthermore, the decomposition tree usually takes very small space. We make the following contributions:1. We define a new concept of module, based on which we propose a multilevel graph compression scheme that compresses graphs into a smaller graph G c . 2. We organize the modules into a hierarchical structure called modular decomposition tree, and propose an efficient algorithm to utilize the tree to answer reachability queries. 3. We conduct extensive experiments with real-world graphs as well as synthetic graphs that demonstrate the advantages of our proposed approach. 4. We provide a discussion on similar compression strategies for k-reachability queries.The remainder of this paper is organized as follows. We first discuss related works in Sect. 2 and present the preliminaries in Sect. 3. Then, we give an overview of our approach and provide the theoretical foundations in Sect. 4, followed by the detailed algorithms in Sect. 5. Our experimental results are given in Sect. 6. Section 7 discusses the possibility of similar compression on k-reachability queries. We conclude our paper in Sect. 8. Related WorkAs briefly mentioned in Sect. 1, existing approaches for answering reachability queries can be classified into indexbased and compression-based approaches.

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“…Existing work on P2P reachability indexing combines graph traversal with vertex-label based pruning in order to be scalable, such as [43,34]. Due to the requirement of graph traversal, the graph and the vertex labels have to reside in main memory, and for massive graphs, one has to resort to a distributed main-memory system.…”

Section: P2p Reachabilitymentioning

confidence: 99%

A general-purpose query-centric framework for querying big graphs

et al. 2016

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Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly "think like a vertex" programming interface to users, and exhibit good horizontal scalability. However, these systems are designed for tasks where the majority of graph vertices participate in computation, but are not suitable for processing lightworkload graph queries where only a small fraction of vertices need to be accessed. The programming paradigm adopted by these systems can seriously under-utilize the resources in a cluster for graph query processing. In this work, we develop a new open-source system, called Quegel, for querying big graphs, which treats queries as first-class citizens in the design of its computing model. Users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.

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Reachability querying: an independent permutation labeling approach

Cited by 48 publications

References 39 publications

Reachability and time-based path queries in temporal graphs

Reachability and time-based path queries in temporal graphs

Modular Decomposition-Based Graph Compression for Fast Reachability Detection

A general-purpose query-centric framework for querying big graphs

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