Link prediction to optimize network performance is of great significance in evolution. Because the complexity systems and uncertainty evolution, it faces many challenges. This paper proposes a new link method based on neural networks trained scale-free as input data, optimized by models output data. In order solve influence generalization experiments, greedy pruning strategy applied. We conside...

After observing a snapshot of social network, link prediction (LP) algorithm identifies node pairs between which new edges will likely materialize in future. Most LP algorithms estimate score for currently non-neighboring pairs, and rank them by this score. Recent systems compute comparing dense, low dimensional vector representations nodes. Graph neural networks (GNNs), particular graph convol...

Many social networks have a bipartite nature. Link prediction in has been the focus of interest for many researchers recently. Network embedding, which maps each node network to low-dimensional feature vector is used solve problems. The aim this study investigate how embedding enhance link performance networks. A and supervised learning based model presented input learned vectors pairs obtained...