This chapter explores advanced applications of network machine learning for multiple networks. We introduce anomaly detection in time series of networks, identifying significant structural changes over time. The chapter then focuses on signal subnetwork estimation for network classification tasks. We present both incoherent and coherent approaches, with incoherent methods identifying edges that best differentiate between network classes, and coherent methods leveraging additional network structure to improve classification accuracy. Practical applications, such as classifying brain networks, are emphasized throughout. These techniques apply to collections of networks, providing a toolkit for analyzing and classifying complex, multinetwork datasets. By integrating previous concepts with new methodologies, we offer a framework for extracting insights and making predictions from diverse network structures with associated attributes.