A New Link Prediction in Directed Networks Based on Attributes Fusion

Abstract

Link prediction, which utilizes the information of endpoint and network structure to predict the unknown links between two nodes, has attracted much attention in recent years. The network topological attributes contain the structure attributes and node attributes. However, some existing methods focus on the node attributes, while others focus on the structure attributes. To solve this problem, we propose a prediction method based on attributes fusion which combines node attributes and structure attributes. In our proposed method, we first analyze the structural attributes based on common neighbors in directed networks and define the structural attribute similarity. Then the similarity contribution of the influence of the common neighbors to the predicted nonadjacent nodes is analyzed. Experimental results on 9 directed networks show that our proposed index achieves higher performance than existing mainstream baselines under the precision evaluation.