Title:
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STRUM: A SEMANTIC TRUST-BASED MINER MODEL
FOR DETECTING INFLUENTIAL SPREADERS IN SOCIAL
NETWORKS |
Author(s):
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Heba M. Wagih, Hoda M. O. Mokhtar and Samy S. Ghoniemy |
ISBN:
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978-989-8533-92-0 |
Editors:
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Ajith P. Abraham and Jörg Roth |
Year:
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2019 |
Edition:
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Single |
Keywords:
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Social Networks, Recommendation Systems, Influential Spreaders |
Type:
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Full Paper |
First Page:
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77 |
Last Page:
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84 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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Social Networks have become embedded in every aspect of today peoples life. The success of online social networks has
a significant role in shaping trust and influence among participants. They are continuing to gain ground every day, opening
the opportunity for several research movements. Great attention is now given for identifying influential spreaders in many
application domains as recommendation systems, viral marketing, epidemic control and others. Most of the recent research
focused only on the network structure with its geospatial information disregarding the semantics underlying these networks.
In this paper, we propose a semantic trust-based miner model (STRUM) that efficiently detects influential spreaders in
social networks for enhancing recommendation services. STRUM considers both geospatial information and semantic
information in defining each user in the social network. Different experiments have been conducted to verify and validate
the proposed algorithm against the latest published using two real location based social networks namely; Brightkite and
Weeplaces are used in the experiments. |
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