| Autor: |
Adnan, T M Tariq, Islam, Md Saiful, Papon, Tarikul Islam, Nath, Shourav, Adnan, Muhammad Abdullah |
| Zdroj: |
Social Network Analysis & Mining; 2/20/2022, Vol. 12 Issue 1, p1-21, 21p |
| Abstrakt: |
Efficient spreading of important information through social media can be highly beneficial, while quick spreading of false content is alarming. Finding the users who are the most influential at information spreading can help develop efficient strategies. However, with the increasing growth of gigantic social networks, existing methods either lack accuracy or have high latency, sometimes being infeasible within limited memory. In this study, we find that rich user-specific information can guide us toward designing more effective methods. We propose UACD, a novel method for identifying the most influential spreaders on the Twitter social network by combining both user-specific and topological information. We provide a distributed implementation of our proposed algorithm on the Amazon EC2 and compare our ranking result with the state-of-the-art methods. Results suggest that UACD is scalable and can process a very large network while being on average 12.5 % more accurate and 175 × faster. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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