基于聚类能力的无参数相似性指标及其在复杂网络链接预测中的应用
A parameter free similarity index based on clustering ability for link prediction in complex networks
摘要：
基于拓扑信息的复杂网络链接预测是一个具有挑战性的问题。在本文中，我们提出了一种新的相似性指数，这是有效的和参数的基础上，聚类能力。在这里，聚类能力被定义为具有相同度的节点的平均聚类系数。我们的想法的动机是，共同的邻居能够有助于形成一个链接的可能性，因为他们拥有一定的能力，他们的邻居聚集在一起，然后这里定义的聚类能力是衡量这种能力。在真实网络和模型网络上的实验数值模拟表明，与三个著名的基于共同邻居的相似性指数：CN，AA和RA相比，新的相似性指数的高精度和高效率。
英文摘要：
Link prediction in complex network based on solely topological information is a challenging problem. In this paper, we propose a novel similarity index, which is efficient and parameter free, based on clustering ability. Here clustering ability is defined as average clustering coefficient of nodes with the same degree. The motivation of our idea is that common-neighbors are able to contribute to the likelihood of forming a link because they own some ability of clustering their neighbors together, and then clustering ability defined here is a measure for this capacity. Experimental numerical simulations on both real-world networks and modeled networks demonstrated the high accuracy and high efficiency of the new similarity index compared with three well-known common-neighbor based similarity indices: CN, AA and RA.
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。
