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https://ruomo.lib.uom.gr/handle/7000/238
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Souravlas, Stavros | - |
dc.contributor.author | Sifaleras, Angelo | - |
dc.contributor.author | Katsavounis, Stefanos | - |
dc.date.accessioned | 2019-10-29T09:33:09Z | - |
dc.date.available | 2019-10-29T09:33:09Z | - |
dc.date.issued | 2018 | - |
dc.identifier | 10.1109/ACCESS.2018.2880157 | en_US |
dc.identifier.issn | 2169-3536 | en_US |
dc.identifier.uri | https://doi.org/10.1109/ACCESS.2018.2880157 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/238 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.source | IEEE Access | en_US |
dc.subject | FRASCATI::Natural sciences::Computer and information sciences | en_US |
dc.subject | FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | en_US |
dc.subject.other | Social networks | en_US |
dc.subject.other | Community detection | en_US |
dc.subject.other | Distributed systems | en_US |
dc.subject.other | Data replication | en_US |
dc.title | A Novel, Interdisciplinary, Approach for Community Detection Based on Remote File Requests | en_US |
dc.type | Article | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
local.identifier.volume | 6 | en_US |
local.identifier.firstpage | 68415 | en_US |
local.identifier.lastpage | 68428 | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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A_novel,_interdisciplinary,_approach_for_community_detection_based_on_remote_file_requests.pdf | 7,23 MB | Adobe PDF | Προβολή/Ανοιγμα |
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