  Clustering is a fundamental problem in network analysis that finds closely
connected groups of nodes and separates them from other nodes in the graph,
while link prediction is to predict whether two nodes in a network are likely
to have a link. The definition of both naturally determines that clustering
must play a positive role in obtaining accurate link prediction tasks. Yet
researchers have long ignored or used inappropriate ways to undermine this
positive relationship. In this article, We construct a simple but efficient
clustering-driven link prediction framework(ClusterLP), with the goal of
directly exploiting the cluster structures to obtain connections between nodes
as accurately as possible in both undirected graphs and directed graphs.
Specifically, we propose that it is easier to establish links between nodes
with similar representation vectors and cluster tendencies in undirected
graphs, while nodes in a directed graphs can more easily point to nodes similar
to their representation vectors and have greater influence in their own
cluster. We customized the implementation of ClusterLP for undirected and
directed graphs, respectively, and the experimental results using multiple
real-world networks on the link prediction task showed that our models is
highly competitive with existing baseline models. The code implementation of
ClusterLP and baselines we use are available at
https://github.com/ZINUX1998/ClusterLP.
