Efficient continual cohesive subgraph search in large temporal graphs
Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and...
Ausführliche Beschreibung
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| Autor*in: |
Li, Yuan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Cohesive subgraph search (CSS) |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: World wide web - Springer US, 1998, 24(2021), 5 vom: 15. Juli, Seite 1483-1509 |
|---|---|
| Übergeordnetes Werk: |
volume:24 ; year:2021 ; number:5 ; day:15 ; month:07 ; pages:1483-1509 |
| Links: |
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| DOI / URN: |
10.1007/s11280-021-00917-z |
|---|
| Katalog-ID: |
OLC2077223111 |
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| 520 | |a Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a ( ,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. | ||
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| 700 | 1 | |a Liu, Jinsheng |4 aut | |
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10.1007/s11280-021-00917-z doi (DE-627)OLC2077223111 (DE-He213)s11280-021-00917-z-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Li, Yuan verfasserin aut Efficient continual cohesive subgraph search in large temporal graphs 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. ( )-continual -core model Cohesive subgraph search (CSS) Exact and approximate algorithms Large temporal graphs Liu, Jinsheng aut Zhao, Huiqun aut Sun, Jing aut Zhao, Yuhai (orcid)0000-0002-1080-0859 aut Wang, Guoren aut Enthalten in World wide web Springer US, 1998 24(2021), 5 vom: 15. Juli, Seite 1483-1509 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:24 year:2021 number:5 day:15 month:07 pages:1483-1509 https://doi.org/10.1007/s11280-021-00917-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 24 2021 5 15 07 1483-1509 |
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10.1007/s11280-021-00917-z doi (DE-627)OLC2077223111 (DE-He213)s11280-021-00917-z-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Li, Yuan verfasserin aut Efficient continual cohesive subgraph search in large temporal graphs 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. ( )-continual -core model Cohesive subgraph search (CSS) Exact and approximate algorithms Large temporal graphs Liu, Jinsheng aut Zhao, Huiqun aut Sun, Jing aut Zhao, Yuhai (orcid)0000-0002-1080-0859 aut Wang, Guoren aut Enthalten in World wide web Springer US, 1998 24(2021), 5 vom: 15. Juli, Seite 1483-1509 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:24 year:2021 number:5 day:15 month:07 pages:1483-1509 https://doi.org/10.1007/s11280-021-00917-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 24 2021 5 15 07 1483-1509 |
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10.1007/s11280-021-00917-z doi (DE-627)OLC2077223111 (DE-He213)s11280-021-00917-z-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Li, Yuan verfasserin aut Efficient continual cohesive subgraph search in large temporal graphs 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. ( )-continual -core model Cohesive subgraph search (CSS) Exact and approximate algorithms Large temporal graphs Liu, Jinsheng aut Zhao, Huiqun aut Sun, Jing aut Zhao, Yuhai (orcid)0000-0002-1080-0859 aut Wang, Guoren aut Enthalten in World wide web Springer US, 1998 24(2021), 5 vom: 15. Juli, Seite 1483-1509 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:24 year:2021 number:5 day:15 month:07 pages:1483-1509 https://doi.org/10.1007/s11280-021-00917-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 24 2021 5 15 07 1483-1509 |
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10.1007/s11280-021-00917-z doi (DE-627)OLC2077223111 (DE-He213)s11280-021-00917-z-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Li, Yuan verfasserin aut Efficient continual cohesive subgraph search in large temporal graphs 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. ( )-continual -core model Cohesive subgraph search (CSS) Exact and approximate algorithms Large temporal graphs Liu, Jinsheng aut Zhao, Huiqun aut Sun, Jing aut Zhao, Yuhai (orcid)0000-0002-1080-0859 aut Wang, Guoren aut Enthalten in World wide web Springer US, 1998 24(2021), 5 vom: 15. Juli, Seite 1483-1509 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:24 year:2021 number:5 day:15 month:07 pages:1483-1509 https://doi.org/10.1007/s11280-021-00917-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 24 2021 5 15 07 1483-1509 |
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10.1007/s11280-021-00917-z doi (DE-627)OLC2077223111 (DE-He213)s11280-021-00917-z-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Li, Yuan verfasserin aut Efficient continual cohesive subgraph search in large temporal graphs 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. ( )-continual -core model Cohesive subgraph search (CSS) Exact and approximate algorithms Large temporal graphs Liu, Jinsheng aut Zhao, Huiqun aut Sun, Jing aut Zhao, Yuhai (orcid)0000-0002-1080-0859 aut Wang, Guoren aut Enthalten in World wide web Springer US, 1998 24(2021), 5 vom: 15. Juli, Seite 1483-1509 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:24 year:2021 number:5 day:15 month:07 pages:1483-1509 https://doi.org/10.1007/s11280-021-00917-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 24 2021 5 15 07 1483-1509 |
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efficient continual cohesive subgraph search in large temporal graphs |
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Efficient continual cohesive subgraph search in large temporal graphs |
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Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract Temporal graphs are equipped with entities and the relationships between entities associated with time stamps. Cohesive subgraph mining (CSM) is a fundamental task in temporal graph analysis, which has gathered great research interests. It benefits from reflecting the dynamism of graphs and has many real-world applications. Yet, most existing work focus on the cohesive subgraph detection (CSD) problem, which identifies all the defined subgraphs in the entire temporal graphs. When graph size becoming too large, it is impractical. In this paper, we are the first to concern about the cohesive subgraph search (CSS) problem in large temporal graphs. In specific, given a query vertex, we are seeking the continual densely connected subgraph including the query vertex. To this end, (1) we model the cohesive subgraph in temporal graphs as a (𝜃,τ)-continual k-core and prove its NP-hardness; (2) we develop two exact algorithms based on different vertex enumeration strategies, called Exact-VD and Exact-VE, respectively. Exact-VD uses depth-first search to find the target subgraphs in a top-down way by gradually deleting vertices from the current subgraph; while Exact-VE starts from the query vertex and continuously expands the ranked vertices in the candidate group until reaching the target subgraphs. Meanwhile, several elegant pruning rules are designed to reduce the search space; (3) to further speed up, we propose an efficient approximate local search method, called Approx-LS, which greedily expands the current subgraph guided by the developed heuristic functions until identifying the results. Comprehensive experiments on four real-life datasets verify the efficiency and effectiveness of our proposed approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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