Predicting future links with new nodes in temporal academic networks

Abstract

Abstract Most real-world systems evolve over time in which entities and the interactions between entities are added and removed—new entities or relationships appear and old entities or relationships vanish. While most network evolutionary models can provide an iterative process for constructing global properties, they cannot capture the evolutionary mechanisms of real systems. Link prediction is hence proposed to predict future links which also can help us understand the evolution law of real systems. The aim of link prediction is to uncover missing links from known parts of the network or quantify the likelihood of the emergence of future links from current structures of the network. However, almost all existing studies ignored that old nodes tend to disappear and new nodes appear over time in real networks, especially in social networks. It is more challenging for link prediction since the new nodes do not have pre-existing structure information. To solve the temporal link prediction problems with new nodes, here we take into account nodal attribute similarity and the shortest path length, namely, ASSPL, to predict future links with new nodes. The results tested on scholar social network and academic funding networks show that it is highly effective and applicable for ASSPL in funding networks with time-evolving. Meanwhile, we make full use of an efficient parameter to exploit how network structure or nodal attribute has an impact on the performance of temporal link prediction. Finally, we find that nodal attributes and network structure complement each other well for predicting future links with new nodes in funding networks.