在无尺度网络上通用程度偏差的随机步行
Karan Singh1, Narendran R V1, V K Chandrasekar2
1Indian Institute of Science Education and Research Thiruvananthapuram, School of Physics, Thiruvananthapuram 695551, Kerala, India.
Physical review. E
|January 21, 2026
概括
我们为无尺度网络引入了通用程度偏差随机步行 (GDBRW) 模型. 这种模式通过抑制常见的捕获模式,提高了勘探效率,特别是在稀疏的网络中.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 计算建模 计算建模
背景情况:
- 无规模网络在自然和技术中无处不在.
- 传统的随机步行模型在稀疏的网络中难以提高效率.
- 枢纽叶捕获图案阻碍了有效的网络探索.
研究的目的:
- 为无规模网络开发一个通用度偏差随机步行 (GDBRW) 模型.
- 提高网络勘探效率,特别是在连接性较低的系统中.
- 为了抑制有害的动机,如叶陷.
主要方法:
- 开发一个GDBRW模型,用于过渡概率的可调节指数 (α,β).
- 使用连续性方法推导平衡概率分布.
- 模拟各种网络密度的探索时间.
主要成果:
- 在稀疏网络中,GDBRW模型显著提高了勘探效率.
- 它的表现优于传统的受欢迎程度驱动的随机步行.
- 该模型有效地抑制了回落和叶陷图案.
结论:
- GDBRW为探索无规模网络提供了一个强大而有效的策略.
- 该模型的有效性在连接性较低的环境中尤为明显.
- 对称性驱动的后备抑制是提高覆盖效率的关键.
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