Department of Psychology, University of Milan-Bicocca, Milan, Italy.
PLoS One. 2014 Feb 21;9(2):e88669. doi: 10.1371/journal.pone.0088669. eCollection 2014.
The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data.
近年来，网络分析在人格心理学和精神病理学中的应用提出了新的方法学挑战。人格和精神病理学网络通常基于相关矩阵，因此包括正和负边缘符号。然而，网络分析的一些应用忽略了负边缘，例如计算聚类系数。在本文中，我们说明了在基于相关矩阵的网络中区分正和负边缘的重要性。聚类系数被推广到有符号相关网络中：引入了三个新的指标，它们考虑了边缘符号，每个指标都源自现有的、广泛使用的公式。新指标的性能在一个有符号的模拟网络和一个基于实际人格心理学数据的有符号网络上进行了说明和比较，与无符号指标的性能进行了比较。结果表明，与无符号指标相比，新指标在相关网络中的样本变化更具抵抗力，因此在模拟网络和真实数据中具有更高的收敛性。
