Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks
| Autor: | Georgios Pispirigos, Christos Makris |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Theoretical computer science
social network analysis Big data Stability (learning theory) 02 engineering and technology Network topology lcsh:Technology Management Information Systems Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering community detection supervised machine learning bootstrap resampling Social network analysis Social network business.industry lcsh:T Graph partition Graph theory Computer Science Applications Scalability distributed processing 020201 artificial intelligence & image processing business stacking ensemble learning Information Systems community prediction |
| Zdroj: | Big Data and Cognitive Computing, Vol 5, Iss 14, p 14 (2021) Big Data and Cognitive Computing Volume 5 Issue 1 |
| ISSN: | 2504-2289 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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