基于最低度偏置重启随机游走的链路预测方法

链路预测是数据挖掘主题中的一个重要问题。基于随机游走的相似性方法一般设定游走粒子转移到相邻节点的概率是相等的，忽略了节点度值对转移概率的影响。针对此问题，提出一种基于lowest-degree偏置重启随机游走的链路预测方法。首先引入最低度偏置函数，对游走粒子的转移概率进行重新定义，然后将最低度偏置随机游走策略运用到重启随机游走中，探究粒子在游走过程中最低度偏向策略对节点相似度的影响。在九个真实网络数据集上进行链路预测，结果表明，所提方法具有良好的预测精度，且挖掘了更多网络拓扑结构信息，证明该算法在节点相似性的评估上具有一定的优势。

Link prediction algorithm based on lowest-degree preference random walk with restart

Link prediction is an important issue in the subject of data mining. The similarity algorithm based on random walk often sets the probability of particles transferring to adjacent nodes to be equal, but ignores the effect of node degree on the transition probability. To solve this problem, this paper proposed a link prediction algorithm based on lowest-degree preference random walk with restart. Firstly, the algorithm redefined the transition probability of the walkers by introducing lowest-degree preference function, then applied it to the random walk with restart, and explored the effect of lowest-degree preference strategy on node similarity. The experimental results of nine real networks show that the proposed method has higher prediction accuracy, and gives more network topology information, which proves that the algorithm has certain advantages in the evaluation of node similarity.