An algorithm for the multiple common subgraph problem

Bayada, Denis M.; Simpson, Richard; Johnson, A. Peter

doi:10.1021/ci00010a015

Cited by 37 publications

(25 citation statements)

References 5 publications

(5 reference statements)

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“…For example, mining frequent substructures and subgraph patterns from many graphs (i.e., a graph database) has been studied intensively [4,27,16,17,20,32,31,28]. Yan et al [33] used frequent graph patterns to index graphs.…”

Section: Related Workmentioning

confidence: 99%

On mining cross-graph quasi-cliques

Pei

Jiang

Zhang

2005

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining

208

124

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Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasicliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.

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Section: Related Workmentioning

confidence: 99%

On mining cross-graph quasi-cliques

Pei

Jiang

Zhang

2005

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining

208

124

View full text Add to dashboard Cite

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“…Therefore, the problem reduces to determining if a revealing path from one dangerous edge list is a subgraph of another, for which there are a variety of solutions from graph theory. [2] The final method available to the agent is the mergeDangerousEdgeLists method, which merges the two given dangerous edge lists and returns a merged dangerous edge lists. In this agent, coverage conflict is handled by merging the two conflicting modifications.…”

Section: Data Structures and Supporting Methodsmentioning

confidence: 99%

Concurrency in a decentralized automatic regression test selection framework for web services

Ruth

2008

Proceedings of the 15th ACM Mardi Gras Conference: From Lightweight Mash-Ups to Lambda Grids: Understanding the Spectrum of Dis

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Business processes can be realized by utilizing many different Web services directly or indirectly in workflows which may be internal or external to the enterprise. Since service-oriented architecture allows service providers to modify the underlying implementation of a service at any time, a new set of challenges arise in software testing, especially regression testing. In this work, a framework which automates a key tenet of regression testing, safe regression test selection, for Web services in a decentralized, end-to-end manner using service monitoring will be presented. Any such system must be capable of handling multiple concurrent modifications and the main contribution of this work is defining and resolving those challenges related to concurrency in a framework for automating a decentralized, end-to-end, safe regression testing technique being applied to Web services. The challenges which will be discussed include coverage conflict, fault locatability, test inconsistency, and communication errors. A solution in the form of software agents following a predetermined distributed algorithm will also be presented.

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“…Subgraph isomorphism: A number of people have studied the problem of identifying maximal isomorphic subgraphs [3,15,5,18]. Since this in general is a computationally hard problem, these approaches typically employ heuristics that seem to help especially when the graphs being analyzed are representations of molecules.…”

Section: Related Workmentioning

confidence: 99%

Using Slicing to Identify Duplication in Source Code

Komondoor

Horwitz

2001

Lecture Notes in Computer Science

338

250

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Abstract. Programs often have a lot of duplicated code, which makes both understanding and maintenance more difficult. This problem can be alleviated by detecting duplicated code, extracting it into a separate new procedure, and replacing all the clones (the instances of the duplicated code) by calls to the new procedure. This paper describes the design and initial implementation of a tool that finds clones and displays them to the programmer. The novel aspect of our approach is the use of program dependence graphs (PDGs) and program slicing to find isomorphic PDG subgraphs that represent clones. The key benefits of this approach are that our tool can find non-contiguous clones (clones whose components do not occur as contiguous text in the program), clones in which matching statements have been reordered, and clones that are intertwined with each other. Furthermore, the clones that are found are likely to be meaningful computations, and thus good candidates for extraction.

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An algorithm for the multiple common subgraph problem

Cited by 37 publications

References 5 publications

On mining cross-graph quasi-cliques

On mining cross-graph quasi-cliques

Concurrency in a decentralized automatic regression test selection framework for web services

Using Slicing to Identify Duplication in Source Code

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