UACD: A Local Approach for Identifying the Most Influential Spreaders in Twitter in a Distributed Environment
Published in Social Network Analysis and Mining, 2022
We propose UACD, a novel method for identifying the most influential spreaders in large social networks by combining user-specific attributes with topological structure. Unlike traditional global centrality methods, UACD uses only local node information and offers scalability to large graphs while maintaining superior ranking performance. A distributed implementation on Amazon EC2 shows that UACD is on average $\mathbf{12.5\%}$ more accurate and $\mathbf{175\times}$ faster than state-of-the-art alternatives for influence ranking on large Twitter datasets.
Recommended citation: Adnan, T.T., Islam, M.S., Papon, T.I., Nath, S. and Adnan, M.A., 2022. Uacd: a local approach for identifying the most influential spreaders in twitter in a distributed environment. Social Network Analysis and Mining, 12(1), p.37.
Download Paper