The current link prediction algorithms based on network representation learning mainly construct feature vectors by capturing the neighborhood topology information of network nodes for link prediction. However, those algorithms usually only focus on learning information from the single neighborhood topology of network nodes, while ignore the researches on similarity between multiple nodes in link structure. Aiming at these problems, a new Link Prediction model based on Densely connected convolutional Network (DenseNet-LP) was proposed. Firstly, the node representation vectors were generated by the network representation learning algorithm called node2vec, and the structure information of the network nodes was mapped into three dimensional feature information by these vectors. Then, DenseNet was used to to capture the features of link structure and establish a two-category classification model to realize link prediction. The experimental results on four public datasets show that, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) value of the prediction result of the proposed model is increased by up to 18 percentage points compared to the result of network representation learning algorithm.

Existing Word2vec-based Network Representation Learning (NRL) algorithms use a Random Walk (RW) to generate node sequence. The RW tends to select nodes with larger degrees, so that the node sequence can not reflect the network structure information well, decreasing the performance of the algorithm. To solve the problem, a new network representation learning algorithm based on improved random walk was proposed. Firstly, RLP-MHRW (Remove self-Loop Probability for Metropolis-Hastings Random Walk) was used to generate node sequence. This algorithm would not favor nodes with larger degrees while forming a node sequence, so that the obtained sequence can efficiently reflect the network structure information. Then, the node sequence was put into Skip-gram model to obtain the node representation vector. Finally, the performance of the network representation learning algorithm was measured by a link prediction task. Contrast experiment has been performed in four real network datasets. Compared with LINE (Large-scale Information Network Embedding) and node2vec on arXiv ASTRO-PH, the AUC (Area Under Curve) value of link prediction has increased by 8.9% and 3.5% respectively, and so do the other datasets. Experimental results show that RLP-MHRW can effectively improve the performance of the network representation learning algorithm based on Word2vec.