Abstract: Most existing link prediction methods of directed networks only focus on link direction information and reciprocal link information, but ignore the contribution of node importance and degree correlation clustering, which leads to the decrease of prediction accuracy. To solve these problems, a directed network link prediction algorithm based on node centrality and degree correlation clustering is proposed. Firstly, it uses node centrality to calculate the number of neighbors of any node to measure the influence of node. Secondly, the node degree correlation clustering coefficient method is extended to the directed network to evaluate the clustering ability of nodes, and it is combined with the network coordination coefficient to obtain the high clustering ability of nodes. Finally, a parametric directed network link prediction index is proposed by integrating the above two kinds of information. On the six real world network compared with recent representative methods, the AUPR and AUC of the proposed algorithm are improved 33% and 1.6% respectively.

Key words: directed network, link prediction, node centrality, degree-related clustering coefficient