Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks
Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has becom...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/5/1/14 |
| _version_ | 1797541614208417792 |
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| author | Christos Makris Georgios Pispirigos |
| author_facet | Christos Makris Georgios Pispirigos |
| author_sort | Christos Makris |
| collection | DOAJ |
| description | Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy. |
| first_indexed | 2024-03-10T13:19:25Z |
| format | Article |
| id | doaj.art-87f0d7df58a54b88addd319f11a53503 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T13:19:25Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-87f0d7df58a54b88addd319f11a535032023-11-21T10:10:49ZengMDPI AGBig Data and Cognitive Computing2504-22892021-03-01511410.3390/bdcc5010014Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social NetworksChristos Makris0Georgios Pispirigos1Computer Engineering & Informatics Department, University of Patras, B’ Building, 26504 Patras, GreeceComputer Engineering & Informatics Department, University of Patras, B’ Building, 26504 Patras, GreeceNowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.https://www.mdpi.com/2504-2289/5/1/14community detectioncommunity predictionstacking ensemble learningsupervised machine learningdistributed processingbootstrap resampling |
| spellingShingle | Christos Makris Georgios Pispirigos Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks Big Data and Cognitive Computing community detection community prediction stacking ensemble learning supervised machine learning distributed processing bootstrap resampling |
| title | Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks |
| title_full | Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks |
| title_fullStr | Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks |
| title_full_unstemmed | Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks |
| title_short | Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks |
| title_sort | stacked community prediction a distributed stacking based community extraction methodology for large scale social networks |
| topic | community detection community prediction stacking ensemble learning supervised machine learning distributed processing bootstrap resampling |
| url | https://www.mdpi.com/2504-2289/5/1/14 |
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