Networks are commonly used for representing complex systems, such as interactions between proteins, data communications between computers, and relationships between people. Recently, machine learning methods have achieved impressive performance for a variety of data analysis tasks. This talk presents machine learning methods for visualizing and analyzing network data. First, I present a method designed to quickly visualize a network in a chosen layout based on computing its structural similarity with a database of pre-laid-out networks using graph kernels. Second, I introduce a technique to systematically generate visualizations of a network in diverse layouts using deep generative models. In summary, this talk presents new approaches to depicting networks where users can effortlessly obtain effective visualization of a network without expert knowledge.