复杂网络中四边形的度量
Measuring Quadrangle Formation in Complex Networks
摘要：
经典的聚类系数和最近提出的封闭系数从两个不同的角度量化了三角形的形成，分别在一个开放的三元组中焦点节点在中心或在末端。由于许多网络自然富含三角形，因此它们成为描述和分析网络的标准指标。然而，应用它们的优点可能局限于网络，其中三角形相对较少，但富含四边形，如蛋白质-蛋白质相互作用网络，神经网络和食物网。这就产生了其他方法，这些方法可以在我们的旅程中利用四边形来更好地理解不同类型网络中的局部结构及其意义。这里我们提出两个四边形系数，即，的i-四倍系数和o-四倍系数，量化网络中的四边形形成，我们进一步将它们扩展到加权网络。通过对6个不同领域的16个网络的实验，我们首先揭示了两个四边形系数的密度分布，然后分析了它们与节点度的相关性。最后，我们证明了在网络级，增加平均i-quad系数和平均o-quad系数导致网络分类的显着改善，而在节点级，i-quad和o-quad系数是有用的功能，以提高链接预测。
英文摘要：
The classic clustering coefficient and the lately proposed closure coefficient quantify the formation of triangles from two different perspectives, with the focal node at the centre or at the end in an open triad respectively. As many networks are naturally rich in triangles, they become standard metrics to describe and analyse networks. However, the advantages of applying them can be limited in networks, where there are relatively few triangles but which are rich in quadrangles, such as the protein-protein interaction networks, the neural networks and the food webs. This yields for other approaches that would leverage quadrangles in our journey to better understand local structures and their meaning in different types of networks. Here we propose two quadrangle coefficients, i.e., the i-quad coefficient and the o-quad coefficient, to quantify quadrangle formation in networks, and we further extend them to weighted networks. Through experiments on 16 networks from six different domains, we first reveal the density distribution of the two quadrangle coefficients, and then analyse their correlations with node degree. Finally, we demonstrate that at network-level, adding the average i-quad coefficient and the average o-quad coefficient leads to significant improvement in network classification, while at node-level, the i-quad and o-quad coefficients are useful features to improve link prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
