In this talk, I review challenges of collecting and analyzing human social network data. I first discuss trade-offs between the use of roster-based and name-generator-based tools for studying cooperative networks, and highlight the potential of roster-based, network-structured economic games (e.g., the RICH economic games introduced by Gervais 2017) to address anthropological questions. I then introduce the DieTryin R package, and illustrate its improved scalability over roster-based methods. In cases where network data are collected via self-reports, rather than via experimental games, reported ties may be seriously biased. Individuals may, for example, report making cooperative transfers that did not really occur, or forget to mention real transfers. Many network-level properties are exquisitely sensitive to these biases, and there remains a dearth of easily deployed statistical tools that account for them. To address this issue, I introduce a latent network model, and associated R package, STRAND, that allows one to jointly estimate parameters measuring reporting biases and a latent, underlying true social network. Finally, I present a case study in the use of these tools in a study investigating how inequality and perceptions of inequality influence expression of parochialism versus magnanimity in two mutli-ethnic Colombian communities.