Distributed Reachability Queries on Massive Graphs

Zhang, Tianming; Gao, Yunjun; Li, Congzheng; Ge, Congcong; Guo, Weican; Zhou, Qiang

doi:10.1007/978-3-030-18590-9_55

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Other Reachability Approaches: Besides approaches that use (partial) 2-hop labels, researchers also proposed other approaches that do not involve 2-hop labels, including [9], [12], [14]- [16], [18], [21]. These approaches assign each node u a label that maintains partial TC.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Reachability Ratio Computation for 2-hop Labeling Scheme

Zhou¹,

Tang²,

Du³

2022

Preprint

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As one of the fundamental graph operations, reachability queries processing has been extensively studied during the past decades. Many approaches followed the line of designing 2-hop labels to make acceleration. Considering that the index size cannot be bounded when using all nodes to construct 2-hop labels, researchers proposed to use a part of important nodes to construct 2-hop labels (partial 2-hop labels) to cover as much reachability information as possible. Then, we may achieve better query performance with limited index size and index construction time. However, partial 2-hop labels do not always perform well on different graphs. In this paper, we focus on the problem of how to efficiently compute reachability ratio, such that to help users determine whether partial 2-hop labels should be used to answer reachability queries for the given graph. Intuitively, reachability ratio denotes the ratio of the number of reachable queries that can be answered by partial 2-hop labels over the total number of reachable queries involved in the given graph. We discuss the difficulties of reachability ratio computation, and propose an incremental-partition algorithm for reachability ratio computation. We show by rich experimental results that our algorithm can efficiently get the result of reachability ratio, and show how the overall query performance is affected by different partial 2-hop labels. Based on the experimental results, we give out our findings on whether partial 2-hop labels should be used to the given graph for reachability queries processing.

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Section: Related Workmentioning

confidence: 99%

“…and A i (lines [16][17][18][19][20][21][22][23][24][25]. At the same time, we generate their partitions D(i) and A(i), for which each subset is recorded in H D and H A , respectively.…”

Section: Partitions Computationmentioning

confidence: 99%

Efficient Reachability Ratio Computation for 2-hop Labeling Scheme

Zhou¹,

Tang²,

Du³

2022

Preprint

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“…We use n and m to denote the number of vertices and edges of the graph, respectively. Also, many indexing methods have been proposed based on the idea of compressing the TC [42,45], online search guided by precomputed indices [41,30,50,53,59], and labeling schemes [12,14,16,31,40,52]. The 2-hop approach cannot be directly modified to LCR queries as the classical one does not consider labels.…”

Section: Reachability Queriesmentioning

confidence: 99%

“…This problem seems simple and fundamental but very challenging due to the increasing size of graph data. In recent years, many research efforts have been devoted to efficiently support reachability queries on large graphs (e.g., [12,14,16,31,40,52,41,30,50,53,59]). Most of these techniques only consider the structure information of the graph and all edges are involved in the reachability queries.…”

Section: Introductionmentioning

confidence: 99%

Answering billion-scale label-constrained reachability queries within microsecond

et al. 2020

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In this paper, we study the problem of label-constrained reachability (LCR) query which is fundamental in many applications with directed edge-label graphs. Although the classical reachability query (i.e., reachability query without label constraint) has been extensively studied, LCR query is much more challenging because the number of possible label constraint set is exponential to the size of the labels. We observe that the existing techniques for LCR queries only construct partial index for better scalability, and their worst query time is not guaranteed and could be the same as an online breadth-first search (BFS). In this paper, we propose novel label-constrained 2-hop indexing techniques with novel pruning rules and order strategies. It is shown that our worst query time could be bounded by the in-out index entry size. With all these techniques, comprehensive experiments show that our proposed methods significantly outperform the state-of-the-art technique in terms of query response time (up to 5 orders of magnitude speedup), index size and index construction time. In particular, our proposed method can answer LCR queries within microsecond over billion-scale graphs in a single machine.

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“…The Label+G approaches assign each node u a label L u that covers partial reachability information. The recent Label+G approaches include GRAIL [31], [32], Yes-GRAIL [33], FERRARI-G [34], FELINE [35], IP + [25] and BFL + [26]. Given a query u?…”

Section: ) Dag Traversalmentioning

confidence: 99%

One Edge at a Time: A Novel Approach Towards Efficient Transitive Reduction Computation on DAGs

et al. 2020

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Given a directed acyclic graph (DAG) G, G's transitive reduction (TR) G tr is the unique DAG satisfying that G tr has the minimum number of edges and has the same transitive closure (TC) as G. TR computation has been extensively studied during the past decades and was used in many applications, where the main problem is how to compute TR efficiently for large graphs. However, existing approaches have either large space complexity or higher time complexity, which makes them cannot compute TR efficiently on large dense graphs. We propose a novel approach for TR computation, which takes every single edge as the basic processing unit, and utilizes existing reachability algorithms to test whether it is redundant or not. In this way, we avoid the costly graph traversal operation of existing approaches. We identify the performance bottleneck and propose a set of heuristics to sort edges, such that to reduce the average processing cost of each edge. We show by experimental results that our approach works much better than all the existing approaches, and can be faster than the state-of-the-art approach by more than two orders of magnitude on large dense graphs. INDEX TERMS Graph data management, directed acyclic graph, transitive reduction.

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Distributed Reachability Queries on Massive Graphs

Cited by 7 publications

References 4 publications

Efficient Reachability Ratio Computation for 2-hop Labeling Scheme

Efficient Reachability Ratio Computation for 2-hop Labeling Scheme

Answering billion-scale label-constrained reachability queries within microsecond

One Edge at a Time: A Novel Approach Towards Efficient Transitive Reduction Computation on DAGs

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