A research question that is of interest across many disciplines is whether and how relationships in a network are related to the attributes of the nodes of the network. In this dissertation, we propose two joint frameworks for modeling the relationship between the network and attributes. In the joint latent space model in Chapter 2, shared latent variables are proposed as an integral part of the data generation process for both the network and attributes. In the joint network and item response model in Chapter 3, a set of correlated latent variables is used to explain the dependence between the network and attributes. The former allows us to devise a joint latent space that visually represents the relationship between the network and attributes; and the latter allows us to test and quantify the relationship. We evaluate the proposed models and the corresponding estimation methods through simulation studies and against existing methods. We apply both methods to real-world data sets. Using the joint latent space model, we observe a division in the social circles of the French elites in accordance with the differences in their career, political views, and social status. Using the joint network and item response model, we are able to test and quantify the relationship between the advice-seeking networks of teachers and their perceptions of the school climate. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]