A novel approach for efficient supergraph query processing on graph databases

Zhang, Shuo; Li, Jianzhong; Gao, Hong; Zou, Zhaonian

doi:10.1145/1516360.1516385

Cited by 54 publications

(76 citation statements)

References 24 publications

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“…Using SGI to sequentially check each database graph is computationally expensive for large databases. A filter+verify indexing method has been proposed to quickly process SPS queries [3,4,20,21]. Given a query graph q and a graph database G, the filter sub-routine first prunes false answers and generates a candidate set C(q), where |C(q)| |G|.…”

Section: Filtering With Graph Indexmentioning

confidence: 99%

“…A number of recent studies have proposed algorithms to address the graph search problem [3,5,14,17,19,20,21]. There are two common search scenarios: subgraph search and supergraph search.…”

Section: Introductionmentioning

confidence: 99%

“…In a subgraph search, given a query graph q, the algorithm searches for all graphs that have q as a subgraph, from a graph database [5,14,17,19]. A supergraph search, on the other hand, retrieves all the database graphs that have q as a supergraph [3,4,20,21].…”

Section: Introductionmentioning

confidence: 99%

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Mining and indexing graphs for supergraph search

2013

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We study supergraph search (SPS), that is, given a query graph q and a graph database G that contains a collection of graphs , return graphs that have q as a supergraph from G. SPS has broad applications in bioinformatics, cheminformatics and other scientific and commercial fields. Determining whether a graph is a subgraph (or supergraph) of another is an NP-complete problem. Hence, it is intractable to compute SPS for large graph databases. Two separate indexing methods, a "filter + verify"-based method and a "prefixsharing"-based method, have been studied to efficiently compute SPS. To implement the above two methods, subgraph patterns are mined from the graph database to build an index. Those subgraphs are mined to optimize either the filtering gain or the prefix-sharing gain. However, no single subgraph-mining algorithm considers both gains.This work is the first one to mine subgraphs to optimize both the filtering gain and the prefix-sharing gain while processing SPS queries. First, we show that the subgraph-mining problem is NPhard. Then, we propose two polynomial-time algorithms to solve the problem with an approximation ratio of 1−1/e and 1/4 respectively. In addition, we construct a lattice-like index, LW-index, to organize the selected subgraph patterns for fast index-lookup. Our experiments show that our approach improves the query processing time for SPS queries by a factor of 3 to 10.

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Section: Filtering With Graph Indexmentioning

confidence: 99%

“…A number of recent studies have proposed algorithms to address the graph search problem [3,5,14,17,19,20,21]. There are two common search scenarios: subgraph search and supergraph search.…”

Section: Introductionmentioning

confidence: 99%

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Mining and indexing graphs for supergraph search

2013

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“…[5], [8], [9], [10]). " As for the second category (that is to say search graphs by similarity) it consists in seeking all the graphs of GDB which are structurally similar to the graph of the current request and has emerged as a new trend [11], [12]).…”

Section: Introductionmentioning

confidence: 99%

Similarity Measure of Graphs

Amine¹,

Charkaoui

Abdelbaki³

et al. 2017

Int. J. Recent Contrib. Eng. Sci. IT

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Abstract-The topic of identifying the similarity of graphs was considered as highly recommended research field in the Web semantic, artificial intelligence, the shape recognition and information research. One of the fundamental problems of graph databases is finding similar graphs to a graph query. Existing approaches dealing with this problem are usually based on the nodes and arcs of the two graphs, regardless of parental semantic links. For instance, a common connection is not identified as being part of the similarity of two graphs in cases like two graphs without common concepts, the measure of similarity based on the union of two graphs, or the one based on the notion of maximum common sub-graph (SCM), or the distance of edition of graphs. This leads to an inadequate situation in the context of information research. To overcome this problem, we suggest a new measure of similarity between graphs, based on the similarity measure of Wu and Palmer. We have shown that this new measure satisfies the properties of a measure of similarities and we applied this new measure on examples. The results show that our measure provides a run time with a gain of time compared to existing approaches. In addition, we compared the relevance of the similarity values obtained, it appears that this new graphs measure is advantageous and offers a contribution to solving the problem mentioned above.

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“…Both sub-problems consider the procedure of checking subgraph isomorphism, known to be NP-Complete. Many query processing approaches using indexing techniques have been developed to reduce the search space and then efficiently solve these two sub-problems [8], [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A similarity skyline approach for handling graph queries - A preliminary report

Abbaci

Hadjali

Liétard

et al. 2011

2011 IEEE 27th International Conference on Data Engineering Workshops

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Abstract-One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an alternative approach allowing for searching similar graphs to a graph query where similarity between graphs is rather modeled by a vector of scalars than a unique scalar. To this end, we introduce the notion of similarity skyline of a graph query defined by the subset of graphs of the target database that are the most similar to the query in a Pareto sense. The idea is to achieve a d-dimensional comparison between graphs in terms of local distance (or similarity) measures and to retrieve those graphs that are maximally similar in the sense of the Pareto dominance relation. A diversity-based method for refining the retrieval result is proposed as well.

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A novel approach for efficient supergraph query processing on graph databases

Cited by 54 publications

References 24 publications

Mining and indexing graphs for supergraph search

Mining and indexing graphs for supergraph search

Similarity Measure of Graphs

A similarity skyline approach for handling graph queries - A preliminary report

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