Link prediction is the problem of predicting the location of either unknown and fake links in static networks or future and disappearing links in evolving networks. Link prediction algorithms are useful in gaining insights into different network structures from partial observations of exemplars.

However, existing link prediction algorithms only focus on regular complex networks and are overly dependent on either the closed triangular structure of networks or the so-called preferential attachment phenomenon. The performance of these algorithms on tree-like networks is poor. A high-accuracy link prediction algorithm in tree-like networks can help us understand the mechanism of online information propagation. The study of online information is an important branch of the field of computational communication or computational social science. Understanding the rules of online information or event propagation is still a challenge that is deeply related to social tie (link) prediction, user (node) ranking, community density, etc.

This Special Issue aims to improve link prediction accuracy for tree-like social (propagation) networks, and further our understanding of the online mass computational communication mechanism. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Link prediction for sparse networks
• Null models for link prediction or complex networks
• Online information propagation
• Online mass computational communication
• Community detection for link prediction or information propagation
• Time series or chaos for information propagation
• Information propagation for economic behavior