🤖 AI Summary
Existing methods for identifying influential nodes often suffer from limited accuracy, poor resolution, reliance on tunable parameters, and high computational complexity. This work proposes a parameter-free, interpretable, and efficient gravitational quasi-Laplacian framework that constructs structural representations using node degree and k-shell index, incorporates a quasi-Laplacian operator to capture local topology, and employs a short-range gravitational aggregation mechanism with a fixed radius (R=3) to evaluate node influence. Requiring no parameter tuning, the proposed method significantly outperforms eight state-of-the-art algorithms across nine real-world networks, demonstrating superior performance in identification accuracy, resolution, and computational efficiency.
📝 Abstract
Identifying influential nodes in complex networks is a fundamental challenge with broad applications in areas such as social network analysis, communication infrastructure, transportation systems, and information networks. Existing ranking methods typically rely on combinations of structural features-such as degree, k-shell index, and neighborhood connectivity-to estimate a node's importance. However, many of these approaches suffer from key limitations, including insufficient accuracy, low resolution in distinguishing nodes with similar influence, dependence on tunable parameters, and high computational complexity, which restrict their practicality in large-scale or real-world networks. This study introduces a new ranking framework that integrates a quasi-Laplacian structural measure with a gravity-inspired aggregation process. The core idea is to construct a strengthened representation of each node's structural role using only simple yet informative attributes-namely degree and k-shell index-and then evaluate its local influence through a short-range interaction mechanism. The proposed approach is designed to be free of tunable parameters, interpretable, and computationally efficient, requiring only a small fixed gravity radius (R=3), which makes it suitable for large and diverse networks. Experiments conducted on nine real-world networks and compared against eight state-of-the-art methods demonstrate that the proposed framework consistently outperforms existing techniques in terms of accuracy, resolution, and computational simplicity. These results highlight the effectiveness of the gravity-quasi-Laplacian paradigm as a reliable and scalable tool for identifying influential nodes in complex networks.