Efficiently Indexing Large Sparse Graphs for Similarity Search

Wang, Guoren; Wang, Bin; Yang, Xiaochun; Yu, Ge

doi:10.1109/tkde.2010.28

Cited by 66 publications

(58 citation statements)

References 20 publications

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“…Contributions came from four continents and seven countries. At least six teams (Team 1, 3-5, 7-9) published highly influential papers on string matching problems before [15,19,22,24,26,28,33], while two teams (Team 2 and Team 6) can be considered as newcomers. As Table 1 shows, the techniques used cover a broad range and thus subsume a large fraction of previous research in k-approximate string matching.…”

Section: Competition and Methodologymentioning

confidence: 99%

State-of-the-art in string similarity search and join

et al. 2014

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String similarity search and its variants are fundamental problems with many applications in areas such as data integration, data quality, computational linguistics, or bioinformatics. A plethora of methods have been developed over the last decades. Obtaining an overview of the state-of-the-art in this field is difficult, as results are published in various domains without much cross-talk, papers use different data sets and often study subtle variations of the core problems, and the sheer number of proposed methods exceeds the capacity of a single research group. In this paper, we report on the results of the probably largest benchmark ever performed in this field. To overcome the resource bottleneck, we organized the benchmark as an international competition, a workshop at EDBT/ICDT 2013. Various teams from different fields and from all over the world developed or tuned programs for two crisply defined problems. All algorithms were evaluated by an external group on two machines. Altogether, we compared 14 different programs on two string matching problems (k-approximate search and k-approximate join) using data sets of increasing sizes and with different characteristics from two different domains. We compare programs primarily by wall clock time, but also provide results on memory usage, indexing time, batch query effects and scalability in terms of CPU cores. Results were averaged over several runs and confirmed on a second, different hardware platform. A particularly interesting observation is that disciplines can and should learn more from each other, with the three best teams rooting in computational linguistics, databases, and bioinformatics, respectively.

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Section: Competition and Methodologymentioning

confidence: 99%

State-of-the-art in string similarity search and join

et al. 2014

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“…Thus, the state-of-the-art solutions address the problem in a filter-and-verify fashion: first generate a set of candidates that satisfy necessary conditions of the edit distance constraints, and then verify with edit distance computation. Inspired by the q-gram concept in string similarity queries, κ-AT algorithm [14] defines tree-based q-grams on graphs. For each vertex v, a κ-AT (or a q-gram) is a tree rooted at v with all vertices reachable in κ hops.…”

Section: Prior Workmentioning

confidence: 99%

“…It was followed by a recent effort SEGOS [15] that proposed an indexing and search paradigm based on star structures. Another advance defined q-grams on graphs [14], which was inspired by the idea of q-grams on strings. It builds index by generating tree-based q-grams, and produces candidates against a count filtering condition on the number of common q-grams between graphs.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, state-of-the-art solutions are mainly based on a filter-and-verify strategy, which first generates a set of promising candidates under a looser constraint and then verifies them with the expensive GED computation. Inspired by the q-gram idea for string similarity queries, the notions of tree-based q-gram [14] and path-based q-gram [21] were proposed. Both studies convert the distance constraint to a count filtering condition, i.e., a requirement on the number of common q-grams, based on the observation that if the GED between two graphs is small, the majority of q-grams in one graph are preserved.…”

Section: Introductionmentioning

confidence: 99%

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A partition-based approach to structure similarity search

et al. 2013

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Graphs are widely used to model complex data in many applications, such as bioinformatics, chemistry, social networks, pattern recognition, etc. A fundamental and critical query primitive is to efficiently search similar structures in a large collection of graphs. This paper studies the graph similarity queries with edit distance constraints. Existing solutions to the problem utilize fixed-size overlapping substructures to generate candidates, and thus become susceptible to large vertex degrees or large distance thresholds. In this paper, we present a partition-based approach to tackle the problem. By dividing data graphs into variable-size nonoverlapping partitions, the edit distance constraint is converted to a graph containment constraint for candidate generation. We develop efficient query processing algorithms based on the new paradigm. A candidate pruning technique and an improved graph edit distance algorithm are also developed to further boost the performance. In addition, a cost-aware graph partitioning technique is devised to optimize the index. Extensive experiments demonstrate our approach significantly outperforms existing approaches.

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“…Our basic idea is to break graphs into sub-units (sub-unit is used as a small substructure derived from a graph in our paper), and to index them as filtering features using inverted lists. This idea of structure decomposing is similar to many existing methods for filtering sequences (using q-grams) [11], trees (using binary branches) [12], and graphs (using paths, trees or subgraphs to test for graph isomorphism) [4], [5], [13], [14], [15], [16]. Among these existing methods, filtering is done by performing exact matching on the sub-units and then inferring the edit distance bound through those sub-units that exactly match the queried structure.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Graph Indexing Method

Wang

Ding

Tung

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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Abstract-Graphs are popular models for representing complex structure data and similarity search for graphs has become a fundamental research problem. Many techniques have been proposed to support similarity search based on the graph edit distance. However, they all suffer from certain drawbacks: high computational complexity, poor scalability in terms of database size, or not taking full advantage of indexes. To address these problems, in this paper, we propose SEGOS, an indexing and query processing framework for graph similarity search. First, an effective two-level index is constructed off-line based on sub-unit decomposition of graphs. Then, a novel search strategy based on the index is proposed. Two algorithms adapted from TA and CA methods are seamlessly integrated into the proposed strategy to enhance graph search. More specially, the proposed framework is easy to be pipelined to support continuous graph pruning. Extensive experiments are conducted on two real datasets to evaluate the effectiveness and scalability of our approaches.

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Efficiently Indexing Large Sparse Graphs for Similarity Search

Cited by 66 publications

References 20 publications

State-of-the-art in string similarity search and join

State-of-the-art in string similarity search and join

A partition-based approach to structure similarity search

An Efficient Graph Indexing Method

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