Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/238
Title: | A Novel, Interdisciplinary, Approach for Community Detection Based on Remote File Requests |
Authors: | Souravlas, Stavros Sifaleras, Angelo Katsavounis, Stefanos |
Type: | Article |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering |
Keywords: | Social networks Community detection Distributed systems Data replication |
Issue Date: | 2018 |
Publisher: | IEEE |
Source: | IEEE Access |
Volume: | 6 |
First Page: | 68415 |
Last Page: | 68428 |
Abstract: | Community structures are formed in many real-world networks, e.g., biological or medical groups, student groups, and so on. Communities are perhaps the most important feature of today's networks, since the majority of people who join a network also tend to join one or more communities. Therefore, several researchers find that the detection of hidden communities is a very interesting and challenging research field. Communities are represented as the groups of nodes on a graph, corresponding to users with similar interests. This paper introduces a novel, interdisciplinary, approach for community detection, combining social networks and distributed systems, where remote access to shared files is offered in a networked environment. A new metric, based on data requests, is introduced and used as a measure of the belonging degree of a node in a certain formed community. Two sets of simulations are used to verify our scheme: simulation results on synthetic networks and results derived from real data. |
URI: | https://doi.org/10.1109/ACCESS.2018.2880157 https://ruomo.lib.uom.gr/handle/7000/238 |
ISSN: | 2169-3536 |
Other Identifiers: | 10.1109/ACCESS.2018.2880157 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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A_novel,_interdisciplinary,_approach_for_community_detection_based_on_remote_file_requests.pdf | 7,23 MB | Adobe PDF | View/Open |
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