基于矩阵分解的DeepWalk链路预测算法

现有的链路预测方法的数据来源主要是基于邻居、路径和随机游走的方法，使用的是节点相似性假设或者最大似然估计，尚缺少基于神经网络的链路预测研究。基于神经网络的一些研究表明，基于神经网络的DeepWalk网络表示学习算法可以更加有效地挖掘到网络中的结构特征，已有研究证明DeepWalk等同于分解目标矩阵。因此，提出了一种基于矩阵分解的DeepWalk链路预测算法（LPMF）。该算法首先基于矩阵分解的DeepWalk算法分解得到网络的表示向量；然后通过余弦相似度计算每对节点之间的相似度，构建目标网络的相似度矩阵；最后利用相似度矩阵，在三个真实的引文网络中进行链路预测实验。实验结果表明，提出的链路预测算法性能优于现存的20余种链路预测算法。这充分表明了LPMF能够有效地挖掘网络中节点之间的结构关联性，而且在实际网络的链路预测中能够发挥出较为优异的性能。

Link prediction based on matrix factorization for Deepwalk

The data sources of existing link prediction algorithms are mainly based on neighbors, paths, and random walk methods. The link prediction algorithms use mainly node similarity assumptions or maximum likelihood estimates. The link prediction based on neural network is still absent. Some research achievements based on neural network show that the DeepWalk algorithm based on neural network is an efficient network representation learning algorithm, which can more effectively learn the network structure features in the network. It has been proven that DeepWalk is equivalent to factorize the target matrix. Therefore, this paper proposed a link prediction algorithm(LPMF) based on matrix factorization of DeepWalk. This algorithm based on matrix factorization used the DeepWalk algorithm to get the network representation vectors. And then, it calculated the similarities between node pairs of nodes by the cosine similarity method. Based on that, the similarity matrix of the target network was constructed. Finally, this paper used the similarity matrix to conduct the link prediction experiments on three real-world citation networks. The experimental results show that the new method is superior to the existing 20 kinds of link prediction algorithms, which fully shows that LPMF can effectively find the structural correlation between nodes in the network, and performs a more excellent performance in the actual tasks of link prediction.