A Link Prediction Algorithm Based on GAN

Link prediction, as an important research direction in complicated network analysis, has broad application prospects. However, traditional link prediction algorithms are generally designed by the sparse expression of the adjacency matrix, which is computationally expensive and inefficient, being also unable to run on large-scale networks and to preserve their higher order structural features. To fill this gap, we propose a GAN (generative adversarial network)-based link prediction algorithm. The algorithm layers the network graph, preserving the local features and higher-level structural features of the original network graph, and uses a generative adversarial model to recursively and backwardly obtain the low-dimensional vector form of the vertices in each layer of the network graph as the initialization of the network graph in the previous layer. It then obtains the low-dimensional vector form of all the vertices in the original network graph for link prediction, and the problem of local minima that can be generated by random initialization is solved. The experimental results show that our method is superior to many state-of-the-art algorithms.

Automated summary

Link prediction is an important research direction in complex network analysis with broad application prospects. Traditional link prediction algorithms based on sparse expression of the adjacency matrix suffer from high computational cost, inefficiency, and inability to run on large-scale networks while preserving their higher order structural features. To address this, we propose a GAN-based link prediction algorithm that layers the network graph, preserves local and higher-level structural features, and uses a generative adversarial model to obtain low-dimensional vector forms. Our method outperforms many state-of-the-art algorithms.