Spring-Electrical Models For Link Prediction
This is an incremental approach for network analysis researchers, applying an existing visualization method to link prediction without major breakthroughs.
The authors tackled link prediction by adapting spring-electrical models from network visualization, assuming Euclidean distance in layouts correlates with link probability, and demonstrated its flexibility across undirected, directed, and bipartite networks.
We propose a link prediction algorithm that is based on spring-electrical models. The idea to study these models came from the fact that spring-electrical models have been successfully used for networks visualization. A good network visualization usually implies that nodes similar in terms of network topology, e.g., connected and/or belonging to one cluster, tend to be visualized close to each other. Therefore, we assumed that the Euclidean distance between nodes in the obtained network layout correlates with a probability of a link between them. We evaluate the proposed method against several popular baselines and demonstrate its flexibility by applying it to undirected, directed and bipartite networks.