Finding Influential Users in Online Social Networks: A Tree based Approach
By: Khanday, Ahmad Hilal
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Contributor(s): Hashmy, Rana.
Publisher: New Delhi STM Journals 2018Edition: Vol 5 (3), Sep -Dec.Description: 66-70p.Subject(s): Computer EngineeringOnline resources: Click Here
In:
Journal of artificial intelligence research and advances (JoAIRA)Summary: online social networking, people are getting involved in communications, but the real problem that comes to the mind is whether these communications can bring revolutions, and if so, who are the best people in given social networks that are really holding the remote control i.e. they have the power to bring these revolutions. This problem and the likes can be modelled as detecting influential users in online social networks. There can be many different ways to find influential users in social networks. Here we propose a novel method based upon tree data structure to determine the influential users in online social network. We identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. This approach identifies the specific users who most influence others’ activity and does so considerably better than other methods.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2021-2021401 |
online social networking, people are getting involved in communications, but the real problem that comes to the mind is whether these communications can bring revolutions, and if so, who are the best people in given social networks that are really holding the remote control i.e. they have the power to bring these revolutions. This problem and the likes can be modelled as detecting influential users in online social networks. There can be many different ways to find influential users in social networks. Here we propose a novel method based upon tree data structure to determine the influential users in online social network. We identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. This approach identifies the specific users who most influence others’ activity and does so considerably better than other methods.
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