1) The document discusses link prediction in social networks, which aims to predict missing or new links based on analyzing the evolution of social networks. 2) It reviews different approaches for link prediction, including supervised learning methods like decision trees and support vector machines, as well as unsupervised methods based on structural features. 3) Various metrics for analyzing social network structure are described, like centralization, adjacency, closeness, and clustering coefficient, which can be used as features for link prediction models.