基于期望拉普拉斯谱的图生成进化算法
Evolutionary Algorithms for Generating Graphs Matching Desired Laplacian Spectra
AI总结：
本文提出一种基于拉普拉斯谱描述符的进化算法，用于生成具有特定属性的图。通过进化算法生成不同图类的图，使其在非谱指标上具有差异性，实验表明该方法在多种图类和谱范围内有效。
摘要：
具有多样结构特征的图在建模和优化任务中起着核心作用。生成展示共享属性的不同类型的图对于算法选择和配置同样至关重要。然而，构造在广泛图类范围内保持高层属性的图仍然是一个挑战。我们提出了一种新颖的进化方法，通过拉普拉斯图谱描述符来进化图。该描述符可作为适应度函数的一部分，用于根据期望的高层属性评估图。我们的进化算法使图向该描述符进化，以获得一致但在非谱图指标（如路径长度、聚类系数和介数中心性）上不同的图。我们的实验结果表明，该方法在不同图类和广泛拉普拉斯图谱范围内均有效。
英文摘要：
Graphs with diverse structural characteristics play a central role in modelling and optimization tasks. The ability to generate different types of graphs that exhibit shared properties is likewise essential for algorithm selection and configuration. However, constructing graphs that preserve high-level properties across a broad range of graph classes remains a challenging problem. We present a novel evolutionary approach to evolve graphs based on the Laplacian graph spectra descriptor. This descriptor can be used as part of a fitness function to evaluate graphs according to their desired high-level properties. Our evolutionary algorithm evolves graphs towards this descriptor in order to obtain graphs having properties that are consistent with it but are different from each other in terms of non-spectral graph metrics, such as path length, clustering coefficient and betweenness centrality. Our experimental results show that our approach is successful for different classes of graphs and a wide range of Laplacian graph spectra.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
