  Networks can represent a wide range of complex systems, such as social,
biological and technological systems. Link prediction is one of the most
important problems in network analysis, and has attracted much research
interest recently. Many link prediction methods have been proposed to solve
this problem with various technics. We can note that clustering information
plays an important role in solving the link prediction problem. In previous
literatures, we find node clustering coefficient appears frequently in many
link prediction methods. However, node clustering coefficient is limited to
describe the role of a common-neighbor in different local networks, because it
can not distinguish different clustering abilities of a node to different node
pairs. In this paper, we shift our focus from nodes to links, and propose the
concept of asymmetric link clustering (ALC) coefficient. Further, we improve
three node clustering based link prediction methods via the concept of ALC. The
experimental results demonstrate that ALC-based methods outperform node
clustering based methods, especially achieving remarkable improvements on food
web, hamster friendship and Internet networks. Besides, comparing with other
methods, the performance of ALC-based methods are very stable in both
globalized and personalized top-L link prediction tasks.
