Kimura Masahiro, Saito Kazumi, Ueda Naonori
NTT Communication Science Laboratories, 2-4 Hikaridai, Seika-cho, Kyoto 619-0237, Japan.
Neural Netw. 2004 Sep;17(7):975-88. doi: 10.1016/j.neunet.2004.01.005.
In this paper, we propose a new network growth model and its learning algorithm to more precisely model such a real-world growing network as the Web. Unlike the conventional models, we have incorporated directional attachment and community structure for this purpose. We show that the proposed model exhibits a degree distribution with a power-law tail, which is an important characteristic of many large-scale real-world networks including the Web. Using real Web data, we experimentally show that predictive ability can be improved by incorporating directional attachment and community structure. Also, using synthetic data, we experimentally show that predictive ability can definitely be improved by incorporating community structure.
在本文中，我们提出了一种新的网络增长模型及其学习算法，以便更精确地对诸如万维网这样的现实世界增长网络进行建模。与传统模型不同，我们为此纳入了定向附着和社区结构。我们表明，所提出的模型呈现出具有幂律尾部的度分布，这是包括万维网在内的许多大规模现实世界网络的一个重要特征。使用真实的网页数据，我们通过实验表明，纳入定向附着和社区结构可以提高预测能力。此外，使用合成数据，我们通过实验表明，纳入社区结构肯定可以提高预测能力。
