论文标题
复杂网络中相似性和互补性的结构量度
Structural measures of similarity and complementarity in complex networks
论文作者
论文摘要
相似性或同质性的原理通常用于解释在复杂网络中观察到的模式，例如传递性和三角形（3个周期）的丰度。但是，从劳动分裂到蛋白质蛋白质相互作用（PPI）的许多现象都由互补性（差异和协同作用）驱动。在这里，我们表明互补性的原理与四边形（4个循环）和浓密的两部分子图相关。我们将这两个原理与它们的特征主题联系起来，并介绍了两个系数的家族：（1）结构相似性，它们概括了局部聚类和封闭系数，并捕获了相似性驱动的结构的完整范围； （2）结构互补性，类似地定义但基于四边形而不是三角形。使用多个社会和生物网络，我们证明了系数捕获与有意义的领域特异性现象相关的结构特性。我们表明，它们允许区分不同种类的社会关系，并衡量整个生命之树的PPI网络的结构多样性。我们的结果表明，某些类型的关系比同质性更好地解释了互补性，并且可能有助于改善现有的链接预测方法。我们还引入了一个Python软件包，该软件包实施了有效的算法来计算所提出的系数。
The principle of similarity, or homophily, is often used to explain patterns observed in complex networks such as transitivity and the abundance of triangles (3-cycles). However, many phenomena from division of labor to protein-protein interactions (PPI) are driven by complementarity (differences and synergy). Here we show that the principle of complementarity is linked to the abundance of quadrangles (4-cycles) and dense bipartite-like subgraphs. We link both principles to their characteristic motifs and introduce two families of coefficients of: (1) structural similarity, which generalize local clustering and closure coefficients and capture the full spectrum of similarity-driven structures; (2) structural complementarity, defined analogously but based on quadrangles instead of triangles. Using multiple social and biological networks, we demonstrate that the coefficients capture structural properties related to meaningful domain-specific phenomena. We show that they allow distinguishing between different kinds of social relations as well as measuring an increasing structural diversity of PPI networks across the tree of life. Our results indicate that some types of relations are better explained by complementarity than homophily, and may be useful for improving existing link prediction methods. We also introduce a Python package implementing efficient algorithms for calculating the proposed coefficients.
