UACD: User Attributed Core Decomposition for Influential Spreaders

We propose User Attributed Core Decomposition (UACD), a novel graph analytic method that identifies the most influential spreaders in large social networks by combining network topology with rich user-specific information (followers, friends, tweet counts, verified status) in a distributed setting. UACD significantly improves accuracy and scalability over existing local and global centrality measures by incorporating both structural and user attributes.

• Introduced UACN (User Attributed Core Number), a measure that augments traditional k-core decomposition with user features to better capture real influence potential.
• Designed UACD to use only local neighborhood information, avoiding the prohibitive memory/runtime costs of global centrality measures on massive graphs.
• Provided a distributed implementation of the algorithm on AWS EC2, demonstrating scalability to networks with tens of millions of nodes.
• Empirically evaluated UACD against state-of-the-art spreader identification methods using standard metrics (e.g., Kendall τ, Spearman ρ, modified Jaccard similarity), showing ~12.5% higher accuracy on average.
• Achieved up to 175× faster runtime than global centrality approaches while maintaining high ranking quality across real Twitter datasets.