Subgraph similarity maximal all-matching over a large uncertain graph
Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widel...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gu, Yu [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: World wide web - Springer US, 1998, 19(2015), 5 vom: 05. Juli, Seite 755-782 |
|---|---|
| Übergeordnetes Werk: |
volume:19 ; year:2015 ; number:5 ; day:05 ; month:07 ; pages:755-782 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11280-015-0358-9 |
|---|
| Katalog-ID: |
OLC2062248695 |
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| 520 | |a Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency. | ||
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10.1007/s11280-015-0358-9 doi (DE-627)OLC2062248695 (DE-He213)s11280-015-0358-9-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Gu, Yu verfasserin aut Subgraph similarity maximal all-matching over a large uncertain graph 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency. Similarity Subgraph all-matching Uncertain graph Maximal Gao, Chunpeng aut Wang, Lulu aut Yu, Ge aut Enthalten in World wide web Springer US, 1998 19(2015), 5 vom: 05. Juli, Seite 755-782 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:19 year:2015 number:5 day:05 month:07 pages:755-782 https://doi.org/10.1007/s11280-015-0358-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_70 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 19 2015 5 05 07 755-782 |
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10.1007/s11280-015-0358-9 doi (DE-627)OLC2062248695 (DE-He213)s11280-015-0358-9-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Gu, Yu verfasserin aut Subgraph similarity maximal all-matching over a large uncertain graph 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency. Similarity Subgraph all-matching Uncertain graph Maximal Gao, Chunpeng aut Wang, Lulu aut Yu, Ge aut Enthalten in World wide web Springer US, 1998 19(2015), 5 vom: 05. Juli, Seite 755-782 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:19 year:2015 number:5 day:05 month:07 pages:755-782 https://doi.org/10.1007/s11280-015-0358-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_70 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 19 2015 5 05 07 755-782 |
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10.1007/s11280-015-0358-9 doi (DE-627)OLC2062248695 (DE-He213)s11280-015-0358-9-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Gu, Yu verfasserin aut Subgraph similarity maximal all-matching over a large uncertain graph 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency. Similarity Subgraph all-matching Uncertain graph Maximal Gao, Chunpeng aut Wang, Lulu aut Yu, Ge aut Enthalten in World wide web Springer US, 1998 19(2015), 5 vom: 05. Juli, Seite 755-782 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:19 year:2015 number:5 day:05 month:07 pages:755-782 https://doi.org/10.1007/s11280-015-0358-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_70 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 19 2015 5 05 07 755-782 |
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Subgraph similarity maximal all-matching over a large uncertain graph |
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Gu, Yu |
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Gu, Yu Gao, Chunpeng Wang, Lulu Yu, Ge |
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subgraph similarity maximal all-matching over a large uncertain graph |
| title_auth |
Subgraph similarity maximal all-matching over a large uncertain graph |
| abstract |
Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency. © Springer Science+Business Media New York 2015 |
| abstractGer |
Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency. © Springer Science+Business Media New York 2015 |
| abstract_unstemmed |
Abstract Recently, uncertain graph data management and mining techniques have attracted significant interests and research efforts due to potential applications such as protein interaction networks and social networks. Specifically, as a fundamental problem, subgraph similarity all-matching is widely applied in exploratory data analysis. The purpose of subgraph similarity all-matching is to find all the similarity occurrences of the query graph in a large data graph. Numerous algorithms and pruning methods have been developed for the subgraph matching problem over a certain graph. However, insufficient efforts are devoted to subgraph similarity all-matching over an uncertain data graph, which is quite challenging due to high computation costs. In this paper, we define the problem of subgraph similarity maximal all-matching over a large uncertain data graph and propose a framework to solve this problem. To further improve the efficiency, several speed-up techniques are proposed such as the partial graph evaluation, the vertex pruning, the calculation model transformation, the incremental evaluation method and the probability upper bound filtering. Finally, comprehensive experiments are conducted on real graph data to test the performance of our framework and optimization methods. The results verify that our solutions can outperform the basic approach by orders of magnitudes in efficiency. © Springer Science+Business Media New York 2015 |
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Subgraph similarity maximal all-matching over a large uncertain graph |
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Gao, Chunpeng Wang, Lulu Yu, Ge |
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