This chapter focuses on the feature generation problem for graphs and networks. It discusses the feature types. Based on the scope where the features are computed, existing features can be divided into neighborhood-level features and global-level features. The chapter describes the existing feature generation methods and divide them into feature extraction approaches and feature learning approaches. It presents several applications to illustrate feature usages. The chapter discusses the applications of multi-label classification, link prediction, anomaly detection, and visualization. It focuses on the neighborhood-level features as well as their applications in graph analysis tasks. The chapter describes a representative factorization-based method, and discusses its differences from the neural network methods. Different from multi-label prediction, link prediction involves pairs of nodes instead of individual nodes.