I/O Efficient Early Bursting Cohesive Subgraph Discovery in Massive Temporal Networks
Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as re...
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| Vydané v: | Journal of computer science and technology Ročník 37; číslo 6; s. 1337 - 1355 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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Singapore
Springer Nature Singapore
01.12.2022
Springer Springer Nature B.V School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China%School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China%School of Computer,Beijing Institute of Technology,Beijing 100081,China School of Information Science and Technology,North China University of Technology,Beijing 100144,China%School of Information Science and Technology,North China University of Technology,Beijing 100144,China |
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| Abstract | Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the k-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi-external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB. |
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| AbstractList | Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the fc-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate fc-core and then refining the fc-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi- external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB. Keywords early bursting cohesive subgraph (EBCS), I/O efficient algorithm, semi-external model, temporal network Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the k-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi-external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB. Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the fc-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate fc-core and then refining the fc-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi- external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB. Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the k-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi-external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB.Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the k-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi-external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB.The online version contains supplementary material available at 10.1007/s11390-022-2367-3.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11390-022-2367-3. Temporal networks are an effective way to encode temporal information into graph data losslessly. Finding the bursting cohesive subgraph (BCS), which accumulates its cohesiveness at the fastest rate, is an important problem in temporal networks. The BCS has a large number of applications, such as representing emergency events in social media, traffic congestion in road networks and epidemic outbreak in communities. Nevertheless, existing methods demand the BCS lasting for a time interval, which neglects the timeliness of the BCS. In this paper, we design an early bursting cohesive subgraph (EBCS) model based on the k-core to enable identifying the burstiness as soon as possible. To find the EBCS, we first construct a time weight graph (TWG) to measure the bursting level by integrating the topological and temporal information. Then, we propose a global search algorithm, called GS-EBCS, which can find the exact EBCS by iteratively removing nodes from the TWG. Further, we propose a local search algorithm, named LS-EBCS, to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time complexity. Subsequently, considering the situation that the massive temporal networks cannot be completely put into the memory, we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms, namely I/O-GS and I/O-LS respectively, to find the EBCS under the semi-external model. Extensive experiments, conducted on four real temporal networks, demonstrate the efficiency and effectiveness of our proposed algorithms. For example, on the DBLP dataset, I/O-LS and LS-EBCS have comparable running time, while the maximum memory usage of I/O-LS is only 6.5 MB, which is much smaller than that of LS-EBCS taking 308.7 MB. The online version contains supplementary material available at 10.1007/s11390-022-2367-3. |
| Audience | Academic |
| Author | Li, Yuan Fan, Xiao-Lin Dai, Jie Zhao, Yu-Hai Wang, Guo-Ren |
| AuthorAffiliation | School of Information Science and Technology,North China University of Technology,Beijing 100144,China%School of Information Science and Technology,North China University of Technology,Beijing 100144,China;School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China%School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China%School of Computer,Beijing Institute of Technology,Beijing 100081,China |
| AuthorAffiliation_xml | – name: School of Information Science and Technology,North China University of Technology,Beijing 100144,China%School of Information Science and Technology,North China University of Technology,Beijing 100144,China;School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China%School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China%School of Computer,Beijing Institute of Technology,Beijing 100081,China |
| Author_xml | – sequence: 1 givenname: Yuan surname: Li fullname: Li, Yuan organization: School of Information Science and Technology, North China University of Technology – sequence: 2 givenname: Jie surname: Dai fullname: Dai, Jie organization: School of Information Science and Technology, North China University of Technology, School of Computer Science and Engineering, Northeastern University – sequence: 3 givenname: Xiao-Lin surname: Fan fullname: Fan, Xiao-Lin organization: School of Information Science and Technology, North China University of Technology – sequence: 4 givenname: Yu-Hai surname: Zhao fullname: Zhao, Yu-Hai email: zhaoyuhai@mail.neu.edu.cn organization: School of Computer Science and Engineering, Northeastern University – sequence: 5 givenname: Guo-Ren surname: Wang fullname: Wang, Guo-Ren organization: School of Computer, Beijing Institute of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36594008$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_eswa_2025_128234 crossref_primary_10_1016_j_imu_2023_101371 |
| Cites_doi | 10.1109/ICDE.2019.00104 10.1007/s00778-014-0372-z 10.1007/s00778-020-00649-y 10.1016/j.ins.2017.07.012 10.1109/ICDE.2011.5767911 10.1109/ICDE.2018.00077 10.1109/TKDE.2020.3040762 10.1109/TBDATA.2021.3058294 10.1016/j.physrep.2012.03.001 10.1109/ICDM.2008.104 10.1109/TKDE.2020.3028025 10.1007/s10618-018-0602-x 10.1109/SWC50871.2021.00044 10.14778/2735479.2735484 10.1109/TBDATA.2020.2974849 10.11897/SP.J.1016.2020.01721 10.1007/s11280-021-00917-z 10.1145/2983323.2983836 10.1007/s11280-019-00725-6 10.1007/s00778-017-0467-4 10.1109/TBDATA.2019.2908384 10.1109/ICDE.2016.7498235 10.1007/978-3-319-63254-4_24 10.1007/3-540-36574-5_3 10.1145/3340531.3412091 10.1007/s00778-016-0451-4 10.1007/s10618-015-0422-1 10.14778/3358701.3358704 10.1145/2160601.2160616 10.14778/3213880.3213881 10.1016/j.chaos.2020.110057 10.1145/2588555.2612179 |
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| DOI | 10.1007/s11390-022-2367-3 |
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| Keywords | early bursting cohesive subgraph (EBCS) I/O efficient algorithm temporal network semi-external model early bursting cohesive subgraph(EBCS) |
| Language | English |
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| References | L Chu (2367_CR10) 2019; 12 P Sun (2367_CR27) 2020; 6 Y Jiang (2367_CR31) 2021; 30 H Qin (2367_CR4) 2022; 8 Y Li (2367_CR6) 2021; 24 2367_CR19 2367_CR16 Z Zhang (2367_CR30) 2015; 24 2367_CR15 Y Li (2367_CR33) 2020; 43 2367_CR13 2367_CR12 2367_CR11 2367_CR2 2367_CR5 K Semertzidis (2367_CR3) 2019; 33 N Barbieri (2367_CR14) 2015; 29 2367_CR7 2367_CR9 2367_CR8 L Yuan (2367_CR29) 2017; 26 P Holme (2367_CR1) 2012; 519 L Sun (2367_CR22) 2022; 34 2367_CR28 RH Li (2367_CR17) 2015; 8 Z Zheng (2367_CR21) 2017; 417 2367_CR26 2367_CR25 2367_CR23 R Li (2367_CR18) 2017; 26 Y Li (2367_CR32) 2020; 23 F Bi (2367_CR20) 2018; 11 H Qin (2367_CR24) 2022; 34 |
| References_xml | – ident: 2367_CR7 doi: 10.1109/ICDE.2019.00104 – volume: 24 start-page: 245 issue: 2 year: 2015 ident: 2367_CR30 publication-title: VLDB J. doi: 10.1007/s00778-014-0372-z – volume: 30 start-page: 713 issue: 5 year: 2021 ident: 2367_CR31 publication-title: VLDB J. doi: 10.1007/s00778-020-00649-y – volume: 417 start-page: 344 year: 2017 ident: 2367_CR21 publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.07.012 – ident: 2367_CR26 doi: 10.1109/ICDE.2011.5767911 – ident: 2367_CR2 doi: 10.1109/ICDE.2018.00077 – volume: 34 start-page: 4313 issue: 9 year: 2022 ident: 2367_CR22 publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2020.3040762 – ident: 2367_CR5 doi: 10.1109/TBDATA.2021.3058294 – volume: 519 start-page: 97 issue: 3 year: 2012 ident: 2367_CR1 publication-title: Physics Reports doi: 10.1016/j.physrep.2012.03.001 – ident: 2367_CR23 doi: 10.1109/ICDM.2008.104 – volume: 34 start-page: 3927 issue: 8 year: 2022 ident: 2367_CR24 publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2020.3028025 – volume: 33 start-page: 1417 issue: 5 year: 2019 ident: 2367_CR3 publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-018-0602-x – ident: 2367_CR16 doi: 10.1109/SWC50871.2021.00044 – volume: 8 start-page: 509 issue: 5 year: 2015 ident: 2367_CR17 publication-title: Proc. VLDB Endow. doi: 10.14778/2735479.2735484 – volume: 8 start-page: 671 issue: 3 year: 2022 ident: 2367_CR4 publication-title: IEEE Trans. Big Data doi: 10.1109/TBDATA.2020.2974849 – volume: 43 start-page: 1721 issue: 9 year: 2020 ident: 2367_CR33 publication-title: Chinese Journal of Computers doi: 10.11897/SP.J.1016.2020.01721 – volume: 24 start-page: 1483 issue: 5 year: 2021 ident: 2367_CR6 publication-title: World Wide Web doi: 10.1007/s11280-021-00917-z – ident: 2367_CR19 doi: 10.1145/2983323.2983836 – volume: 23 start-page: 799 issue: 2 year: 2020 ident: 2367_CR32 publication-title: World Wide Web doi: 10.1007/s11280-019-00725-6 – ident: 2367_CR9 – volume: 26 start-page: 751 issue: 6 year: 2017 ident: 2367_CR18 publication-title: VLDB J. doi: 10.1007/s00778-017-0467-4 – volume: 6 start-page: 816 issue: 4 year: 2020 ident: 2367_CR27 publication-title: IEEE Trans. Big Data doi: 10.1109/TBDATA.2019.2908384 – ident: 2367_CR28 doi: 10.1109/ICDE.2016.7498235 – ident: 2367_CR11 doi: 10.1007/978-3-319-63254-4_24 – ident: 2367_CR25 doi: 10.1007/3-540-36574-5_3 – ident: 2367_CR8 doi: 10.1145/3340531.3412091 – volume: 26 start-page: 275 issue: 2 year: 2017 ident: 2367_CR29 publication-title: VLDB J. doi: 10.1007/s00778-016-0451-4 – volume: 29 start-page: 1406 issue: 5 year: 2015 ident: 2367_CR14 publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-015-0422-1 – volume: 12 start-page: 2353 issue: 13 year: 2019 ident: 2367_CR10 publication-title: Proc. VLDB Endow. doi: 10.14778/3358701.3358704 – ident: 2367_CR12 doi: 10.1145/2160601.2160616 – volume: 11 start-page: 1056 issue: 9 year: 2018 ident: 2367_CR20 publication-title: Proc. VLDB Endow. doi: 10.14778/3213880.3213881 – ident: 2367_CR13 doi: 10.1016/j.chaos.2020.110057 – ident: 2367_CR15 doi: 10.1145/2588555.2612179 |
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| SubjectTerms | Algorithms Analysis Artificial Intelligence Bursting Cohesion Computer Science Data Structures and Information Theory Epidemics Football (College) Graph theory Information Systems Applications (incl.Internet) Networks Regular Paper Roads & highways Run time (computers) Search algorithms Software Engineering Theory of Computation Traffic congestion |
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