Haruna Taichi, Gunji Yukio-Pegio
Department of Information and Sciences, School of Arts and Sciences, Tokyo Woman's Christian University, 2-6-1 Zempukuji, Suginami-ku, Tokyo, 167-8585, Japan.
Department of Intermedia Art and Science, School of Fundamental Science and Technology, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, 169-8555, Japan.
Sci Rep. 2020 Dec 18;10(1):22351. doi: 10.1038/s41598-020-79318-7.
We propose a growing network model that can generate dense scale-free networks with an almost neutral degree-degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-free networks but with a different higher-order network structure. The modification is mediated by category theory. Category theory can identify a duality structure hidden in the previous model. The proposed model is built so that the identified duality is preserved. This work is a novel application of category theory for designing a network model focusing on a universal algebraic structure.
我们提出了一种增长网络模型，该模型可以生成具有几乎中性度-度相关性和局部聚类系数负标度的密集无标度网络。该模型是通过修改文献中现有的一个模型得到的，该现有模型也可以生成密集无标度网络，但具有不同的高阶网络结构。这种修改是由范畴论介导的。范畴论可以识别隐藏在先前模型中的对偶结构。所提出的模型构建方式是为了保留所识别的对偶性。这项工作是范畴论在设计聚焦于通用代数结构的网络模型方面的一种新颖应用。
