δ-Transitive closures and triangle consistency checking: a new way to evaluate graph pattern queries in large graph databases

Chen, Yi Jen; Guo, Bin; Huang, Xingyue

doi:10.1007/s11227-019-02762-4

Cited by 3 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All three trimming algorithms tend to be efficient when choosing a chunk size between 2 10 and 2 16 . Therefore, in our experiments, we fix the chunk size to 2 12 = 4096 for both workload balance and efficient scheduling.…”

Section: Workload Balancementioning

confidence: 99%

See 1 more Smart Citation

Efficient Parallel Graph Trimming by Arc-Consistency

Guo¹,

Sekerinski²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Given a large data graph, trimming techniques can reduce the search space by removing vertices without outgoing edges. One application is to speed up the parallel decomposition of graphs into strongly connected components (SCC decomposition), which is a fundamental step for analyzing graphs. We observe that graph trimming is essentially a kind of arc-consistency problem, and AC-3, AC-4, and AC-6 are the most relevant arc-consistency algorithms for application to graph trimming. The existing parallel graph trimming methods require worst-case O(nm) time and worst-case O(n) space for graphs with n vertices and m edges. We call these parallel AC-3-based as they are much like the AC-3 algorithm. In this work, we propose AC-4-based and AC-6-based trimming methods. That is, AC-4-based trimming has an improved worst-case time of O(n + m) but requires worst-case space of O(n + m); compared with AC-4-based trimming, AC-6-based has the same worst-case time of O(n + m) but an improved worst-case space of O(n). We parallelize the AC-4-based and AC-6-based algorithms to be suitable for shared-memory multi-core machines. The algorithms are designed to minimize synchronization overhead. For these algorithms, we also prove the correctness and analyze time complexities with the work-depth model.In experiments, we compare these three parallel trimming algorithms over a variety of real and synthetic graphs. Specifically, for the maximum number of traversed edges per worker by using 16 workers, AC-3-based traverses up to 58.3 and 36.5 times more edges than AC-6-based trimming and AC-4-based trimming, respectively. That is, AC-6-based trimming traverses much fewer edges than other methods, which is meaningful especially for implicit graphs. In particular, for the practical running time, AC-6-based trimming achieves high speedups over graphs with a large portion of trimable vertices.

show abstract

Section: Workload Balancementioning

confidence: 99%

“…In numerous applications, like social networks [56], pattern matching [12], communication networks [34], and model verification [27], data is organized into directed graphs with vertices for objects and edges for their relationships. The large size of such graphs motivates graph trimming, i.e.…”

Section: Introductionmentioning

confidence: 99%

Efficient Parallel Graph Trimming by Arc-Consistency

Guo¹,

Sekerinski²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Within the query generation and decomposition task for approximate graph matching outlined in [8], in addition to structural clusters we might want to adopt semantic clusters as well: that is always possible via the unique metric ∼ that is used throughout the process. After the graph combination phase that could be carried out in a multi-way join fashion as outlined in [6], we might consequently obtain graph result clusters via the combination of the intermediate subgraph clusters, thus already subsets of different solutions. These representations of graph collections in such clusters can be then further summarized as one single graph using graph summarization algorithms [3,5]: in particular, we require a clustering and summarization function Γ that, taken as an input a collection of graphs, it will indirectly use the similarity function ∼ for the clustering and then summarizes the clusters using an UDF function.…”

Section: Approximate Graph Matchingmentioning

confidence: 99%

“…In industry [11,30] as well as in academia [9,20,22], current knowledge base and linked data research focuses on automatic graph knowledge base construction and supporting efficient graph queries (SPARQL or Cypher) over graph databases (RDF or Property Graph stores). In addition to these common usecases for graph databases, relational databases might be also be represented as graphs [6], and therefore graph queries might be also supported by relational databases [29]. Such knowledge bases have been grown in popularity in several domain experts and decision makers such as governments and industrial stakeholders [1]: they are interested in querying graph data without necessarily knowing both the data schema and its representation [33].…”

Section: Introductionmentioning

confidence: 99%