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Influence maximization using combined community-level influence score
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AI
DOI：10.1016/j.physa.2026.131367.png)
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Abstract
En 中文
• Defined new local and global influence scores using network community structure for efficient spreader detection. • Proposed a combined influence score with adaptively tuned weights based on network topology. • Introduced a penalty-based overlap avoidance mechanism to select well-separated influential nodes. • Designed the IMCCIS method to support parallel execution, enabling faster processing on large networks. • Demonstrated superior performance of IMCCIS over state-of-the-art heuristic and community-based IM methods on real networks.
Keywords:
Influence maximization
Community structure
Spreader detection
Network topology
Influence score
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