| Autor: |
Meryam Essaid, Hongteak Ju |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Systems, Vol 10, Iss 6, p 203 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2079-8954 |
| DOI: |
10.3390/systems10060203 |
| Popis: |
Community detection is essential in P2P network analysis as it helps identify connectivity structure, undesired centralization, and influential nodes. Existing methods primarily utilize topological data and neglect the rich content data. This paper proposes a technique combining topological and content data to detect communities inside the Bitcoin network using a deep feature representation algorithm and Deep Feedforward Autoencoders. Our results show that the Bitcoin network has a higher clustering coefficient, assortativity coefficient, and community structure than expected from a random P2P network. In the Bitcoin network, nodes prefer to connect to other nodes that share the same characteristics. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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