标题： 神经网络结构特性对机器学习性能的影响 显示英文标题

标题： Effects of structural properties of neural networks on machine learning performance

摘要： 近年来，基于图的机器学习技术，如强化学习和图神经网络，引起了广泛关注。 尽管一些近期的研究开始探索神经网络的图结构与其预测性能之间的关系，但它们往往局限于狭窄范围的模型网络，特别是缺乏如社区等中尺度结构。 我们的工作通过进行更全面的研究推进了这一领域，结合了具有异质度分布和社区结构的现实网络结构，这些是许多真实网络的典型特征。 这些社区结构为网络架构提供了细致的视角。 我们的分析使用了如随机网络和无标度网络这样的模型网络，并与生物神经网络及其子集进行比较以进行更详细的分析。 我们研究了这些结构属性对图像分类任务性能的影响。 我们的发现表明，结构特性在一定程度上确实影响性能。 具体而言，具有连贯且高度互联社区的网络表现出增强的学习能力。 与生物神经网络的比较强调了我们发现的相关性，表明了一个值得进一步探索的有趣联系。 本研究对网络科学和机器学习做出了有意义的贡献，提供了可能激发设计更生物启发式神经网络的见解。 显示英文摘要

摘要： In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention. While some recent studies have started to explore the relationship between the graph structure of neural networks and their predictive performance, they often limit themselves to a narrow range of model networks, particularly lacking mesoscale structures such as communities. Our work advances this area by conducting a more comprehensive investigation, incorporating realistic network structures characterized by heterogeneous degree distributions and community structures, which are typical characteristics of many real networks. These community structures offer a nuanced perspective on network architecture. Our analysis employs model networks such as random and scale-free networks, alongside a comparison with a biological neural network and its subsets for more detailed analysis. We examine the impact of these structural attributes on the performance of image classification tasks. Our findings reveal that structural properties do affect performance to some extent. Specifically, networks featuring coherent, densely interconnected communities demonstrate enhanced learning capabilities. The comparison with the biological neural network emphasizes the relevance of our findings to real-world structures, suggesting an intriguing connection worth further exploration. This study contributes meaningfully to network science and machine learning, providing insights that could inspire the design of more biologically informed neural networks.