AI 中文总结
研究针对复杂网络影响节点识别难题，提出结合拟拉普拉斯结构测度与重力启发聚合过程的新框架，无需可调参数，计算高效，实验证明其在精度、分辨率和计算简单性上优于现有技术。
AI 中文摘要
识别复杂网络中的影响节点是一项具有挑战性的任务，在社交网络分析等领域有广泛应用。现有方法存在关键局限，如精度不足、分辨率低、依赖可调参数及计算复杂度高。本研究引入新框架，将拟拉普拉斯结构测度与重力启发的聚合过程相结合。核心是用度和k壳指数构建节点结构角色强化表示，通过短程交互机制评估局部影响。该方法无可调参数、可解释且计算高效，仅需小固定重力半径（R = 3）。在九个真实网络上的实验表明，该框架在精度、分辨率和计算简单性方面优于现有技术，突出了重力-拟拉普拉斯范式的有效性。
英文摘要
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.
Comments20 pages
DOI：10.22060/miscj.2026.25726.5491
