RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan.
Asia Pacific Center for Theoretical Physics, Pohang, 37673, Republic of Korea.
Sci Rep. 2019 Mar 13;9(1):4310. doi: 10.1038/s41598-019-40990-z.
We introduce a model for the formation of social networks, which takes into account the homophily or the tendency of individuals to associate and bond with similar others, and the mechanisms of global and local attachment as well as tie reinforcement due to social interactions between people. We generalize the weighted social network model such that the nodes or individuals have F features and each feature can have q different values. Here the tendency for the tie formation between two individuals due to the overlap in their features represents homophily. We find a phase transition as a function of F or q, resulting in a phase diagram. For fixed q and as a function of F the system shows two phases separated at F. For F < F large, homogeneous, and well separated communities can be identified within which the features match almost perfectly (segregated phase). When F becomes larger than F, the nodes start to belong to several communities and within a community the features match only partially (overlapping phase). Several quantities reflect this transition, including the average degree, clustering coefficient, feature overlap, and the number of communities per node. We also make an attempt to interpret these results in terms of observations on social behavior of humans.
我们介绍了一种社交网络形成模型，该模型考虑了同质性或个体与相似他人关联和结合的趋势，以及全局和局部连接机制，以及由于人与人之间的社会互动而导致的联系强化。我们将加权社交网络模型推广为节点或个体具有 F 个特征，每个特征可以具有 q 个不同的值。这里，由于特征重叠而导致两个人之间联系形成的趋势表示同质性。我们发现了一个作为 F 或 q 的函数的相变，导致一个相图。对于固定的 q 和作为 F 的函数，系统显示在 F 处分开的两个相。对于 F < F large，在其中可以识别出均匀的、分离良好的社区，其中特征几乎完全匹配（分离相）。当 F 变得大于 F 时，节点开始属于几个社区，并且在一个社区内特征仅部分匹配（重叠相）。几个数量反映了这种转变，包括平均度、聚类系数、特征重叠和每个节点的社区数量。我们还试图根据人类社会行为的观察来解释这些结果。
