Research Paper#Social Network Analysis, Influence Maximization, Community Detection🔬 ResearchAnalyzed: Jan 3, 2026 18:22
Community-Aware Influence Maximization Framework
Published:Dec 30, 2025 04:05
•1 min read
•ArXiv
Analysis
This paper addresses a critical limitation in influence maximization (IM) algorithms: the neglect of inter-community influence. By introducing Community-IM++, the authors propose a scalable framework that explicitly models cross-community diffusion, leading to improved performance in real-world social networks. The focus on efficiency and cross-community reach makes this work highly relevant for applications like viral marketing and misinformation control.
Key Takeaways
- •Addresses the limitation of neglecting inter-community influence in IM algorithms.
- •Introduces Community-IM++, a scalable framework for modeling cross-community diffusion.
- •Achieves near-greedy influence spread with significantly reduced runtime.
- •Outperforms existing community-based and degree-based heuristics.
- •Highly relevant for applications requiring efficiency and cross-community reach.
Reference
“Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime, while outperforming Community-IM and degree heuristics.”