Abstract
Internet of Things (IoT) refers to the complex systems generated by the interconnections among widely available objects. Such interactions generate large networks, whose complexity needs to be addressed to provide suitable computationally efficient approaches. In this article, we propose a distributed local community detection algorithm based on specific properties of community center expansions (DLCD-CCE) for large-scale complex networks. The algorithm is evaluated via a prototype system, based on Spark, to verify its accuracy and scalability. The results demonstrate that compared to the typical local community detection algorithms, DLCD-CCE has better accuracy, stability, and scalability, and effectively overcomes the problem that existing algorithms are sensitive to the location of initial seeds.
Original language | English |
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Article number | 8936466 |
Pages (from-to) | 4607-4615 |
Number of pages | 9 |
Journal | IEEE Internet of Things Journal |
Volume | 7 |
Issue number | 5 |
Early online date | 18 Dec 2019 |
DOIs | |
Publication status | Published - 1 May 2020 |
Keywords
- Complex Networks
- Network dynamics
- Community Detection
- Internet of Things
- Internet of Things (IoT)
- Community detection
- complex networks
- network dynamics
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Prof MARCELLO TROVATI
- Computer Science - Professor of Computer Science
- Health Research Institute
Person: Research institute member, Academic