Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model
Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model
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DOI：
10.1109/ijcnn.2018.8489690
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
F. Grando;L. Lamb
中科院分区：
文献类型：
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作者：
F. Grando;L. Lamb
顶点中心性度量是一种多用途的分析工具，通常用于许多应用环境中，以从图和网络结构属性中检索信息和揭示知识。然而，当运行实时应用或大规模真实的世界网络时，这样的度量的算法在计算资源方面是昂贵的。因此，近似技术已经开发出来，并用于计算在这种情况下的措施。在本文中，我们演示并分析了使用神经网络学习算法来解决此类任务，并将其在解决方案质量和计算时间方面的性能与文献中的其他技术进行了比较。我们的工作提供了一些贡献。我们强调了通过神经学习近似中心的优点和缺点。通过经验方法和统计，我们证明了由Levenberg-Marquardt算法训练的前馈神经网络生成的回归模型不仅是考虑计算资源的最佳选择，而且对于相关应用和大规模网络也达到了最佳的解决方案质量。
Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks.