We identify robust features of trust in social networks; these are features which are discriminating yet uncorrelated and can potentially be used to predict trust formation between agents in other social networks. The features we investigate are based on an agent’s individual properties as well as those based on the agent’s location within the network. In addition, we analyze features which take into account the agent’s participation in other social interactions within the same network. Three datasets were used in our study—Sony Online Entertainment’s EverQuest II game dataset, a large email network with sentiments and the publicly available Epinions dataset. The first dataset captures activities from a complex persistent game environment characterized by several types of in-game social interactions, whereas the second dataset has anonymized information about people’s email and instant messaging communication. We formulate the problem as one of the link predictions, intranetwork and internetwork, in social networks. We first build machine learning models and then perform an ablation study to identify robust features of trust. Results indicate that shared skills and interests between two agents, their level of activity and level of expertise are the top three predictors of trust in a social network. Furthermore, if only network topology information were available, then an agent’s propensity to connect or communicate, the cosine similarity between two agents and shortest distance between them are found to be the top three predictors of trust. In our study, we have identified the generic characteristics of the networks used as well as the features investigated so that they can be used as guidelines for studying the problem of predicting trust formation in other social networks.