The Erdos-Renyi (ER) random network model generates graphs under the assumption that there could exist a link u-v between two nodes u and v irrespective of whether or not the two nodes had a common neighbor before the establishment of the link. As a result, random network graphs generated under the ER model are characteristic of having a low clustering coefficient (a measure of the probability for a link to exist between any two neighbors of a node) and low variation in the node degrees, and hence could not match closely to graphs abstracting real-world networks. In this paper, we propose a random network graph model that gives preference to closing the triangle involving three nodes u, w and v with existing links u-w and w-v (i.e., node v is strictly a two-hop neighbor of node u). Accordingly, when node u is looking for a new link to be setup with some other node x, we consider x along with the two-hop neighbors of u and choose one among these nodes with a probability p_link as the new neighbor of node u. The proposed Two-Hop Neighbor Preference (THNP)-based model generates random graphs whose clustering coefficient decreases with increase in node degree: matching closely to several real-world network graphs that are commonly studied for complex network analysis.

Erdos-Renyi (ER) 随机网络模型在假设两个节点u和v之间可能存在链接u - v 的情况下生成图，而不管这两个节点在链接建立之前是否有共同的邻居。因此，在 ER 模型下生成的随机网络图具有低聚类系数（节点的任意两个邻居之间存在链接的概率的度量）和节点度的低变化的特征，因此可以与抽象现实世界网络的图形不匹配。在本文中，我们提出了一个随机网络图模型，该模型优先关闭涉及三个节点u , w的三角形和v具有现有链路u - w和w - v（即，节点v严格地是节点u的两跳邻居）。因此，当节点u正在寻找与其他节点x建立新链路时，我们将x与u的两跳邻居一起考虑，并以概率为p 的_链路从这些节点中选择一个作为节点u的新邻居. 提出的基于两跳邻居偏好 (THNP) 的模型生成随机图，其聚类系数随着节点度的增加而降低：与通常用于复杂网络分析的几个现实世界网络图紧密匹配。