An Efficient Graph Indexing Method

Wang, Xiaoli; Ding, Xiuli; Tung, Anthony K. H.; Ying, Shanshan; Jin, Hai

doi:10.1109/icde.2012.28

Cited by 92 publications

(81 citation statements)

References 31 publications

(39 reference statements)

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“…• SEGOS, labeled by "S", is an algorithm based on stars, incorporating novel indexing and search strategies [15]. We received the source code from the authors.…”

Section: Comparing With Existing Methodsmentioning

confidence: 99%

“…It establishes a filtering condition on the upper bound of SED(g, q) as τ · max(4, 1 + max(γg, γq)), which is also proportional to the maximum vertex degree. Based on star structures, a two-level index and a cascaded search strategy were presented by SEGOS [15]. While it is superior to star structure in search strategy, the basic filtering principle remains the same.…”

Section: Prior Workmentioning

confidence: 99%

“…The notion of star structures [17] were proposed, and the edit distance constraint can be converted to lower and upper bounds of star structure distance via bipartite matching. 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.…”

Section: Related Workmentioning

confidence: 99%

“…Rather than count common features, [17] developed a method to compute the lower and upper bounds of GED through bipartite matching between the star representations of two graphs. The method was later equipped with a two-level index and a cascaded search strategy to find candidates [15].…”

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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“…• SEGOS, labeled by "S", is an algorithm based on stars, incorporating novel indexing and search strategies [15]. We received the source code from the authors.…”

Section: Comparing With Existing Methodsmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A partition-based approach to structure similarity search

et al. 2013

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“…Similar to the work in [25], we can construct a weighted matrix for each pair of grams from two sequences, and apply the Hungarian algorithm [8,19]. Based on gram mapping distance, we show how a tighter lower bound on the edit distance between two sequences could be achieved.…”

Section: Definition 3 (Gram Mapping Distance)(gmd) Given Two Gram Mumentioning

confidence: 99%

Efficient and effective KNN sequence search with approximate n-grams

et al. 2013

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In this paper, we address the problem of finding k-nearest neighbors (KNN) in sequence databases using the edit distance. Unlike most existing works using short and exact ngram matchings together with a filter-and-refine framework for KNN sequence search, our new approach allows us to use longer but approximate n-gram matchings as a basis of KN-N candidates pruning. Based on this new idea, we devise a pipeline framework over a two-level index for searching KNN in the sequence database. By coupling this framework together with several efficient filtering strategies, i.e. the frequency queue and the well-known Combined Algorithm (CA), our proposal brings various enticing advantages over existing works, including 1) huge reduction on false positive candidates to avoid large overheads on candidate verifications; 2) progressive result update and early termination; and 3) good extensibility to parallel computation. We conduct extensive experiments on three real datasets to verify the superiority of the proposed framework.

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Efficient subgraph search on large anonymized graphs

Ding

Jia

et al. 2018

Concurrency and Computation

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Summary Graph is one of the most important data structures to model social networks and becomes popular to find interesting relationships between individuals. Since graphs may contain sensitive information, data curators usually need to anonymize the graph before publication to prevent individual re‐identification, which thus leads to plenty of anonymized graphs for data sharing and exploration. However, the new structures and properties of anonymized graphs make the traditional graph indexing method inefficient or even invalid for query processing. To address the subgraph query problem over anonymized graph database, in this paper, we first introduce basic concepts about anonymized graphs and subgraph queries, then propose an index structure named Closure+‐tree to process the subgraph query efficiently. In particular, graphs were organized hierarchically that each node is an union of its child nodes under some specified mapping functions. During the processing of subgraph queries, the whole graph descendants will be pruned if their union does not contain the query graph. To evaluate the performance of our proposed Closure+‐tree, extensive experiments are performed on both real and synthetic graph data sets. The experimental results revealed that our index structure can prune up to 80% unqualified graphs with variable size of queries. Furthermore, the size of our index structure is only around a quarter of the entire anonymized graph data set, which indicates good scalability over large data sets.

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An Efficient Graph Indexing Method

Cited by 92 publications

References 31 publications

A partition-based approach to structure similarity search

A partition-based approach to structure similarity search

Efficient and effective KNN sequence search with approximate n-grams

Efficient subgraph search on large anonymized graphs

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