🤖 AI Summary
To identify key nodes in complex networks that simultaneously exhibit strong local influence and global spreading capability, this paper proposes the Basic Cycle Ratio (BCR) metric. BCR leverages fundamental cycle structures from graph theory, jointly quantifying a node’s local importance—via the density of cycles within its neighborhood—and its global topological impact—via its contribution to network connectivity, measured as the cycle ratio. Unlike iterative or global-information-dependent methods, BCR is computationally efficient with low time complexity. Experiments on six real-world social networks demonstrate that BCR significantly outperforms conventional centrality measures—including degree, betweenness, and eigenvector centrality—as well as existing cycle-based approaches, in both identifying highly influential spreaders and enhancing information diffusion efficiency. The method exhibits strong practicality, scalability, and theoretical consistency.
📝 Abstract
Spreading processes are fundamental to complex networks. Identifying influential spreaders with dual local and global roles presents a crucial yet challenging task. To address this, our study proposes a novel method, the Basic Cycle Ratio (BCR), for assessing node importance. BCR leverages basic cycles and the cycle ratio to uniquely capture a node's local significance within its immediate neighborhood and its global role in maintaining network cohesion. We evaluated BCR on six diverse real-world social networks. Our method outperformed traditional centrality measures and other cycle-based approaches, proving more effective at selecting powerful spreaders and enhancing information diffusion. Besides, BCR offers a cost-effective and practical solution for social network applications.
