As those in need increasingly ask for favors in online social services, having a technique to accurately predict whether their requests will be successful can instantaneously help them better formulating the requests. This paper aims to boost the accuracy of predicting the success of altruistic requests, by following the similar setting of the state-of-theart work ADJ . While ADJ has an unsatisfying prediction accuracy and requires a large set of training data, we develop a novel request success prediction model, termed Graph-based Predictor for Request Success (GPRS). Our GPRS model is featured by learning the correlation between success or not and the set of features extracted in the request, together with a label propagation-based optimization mechanism. Besides, in addition to the textual, social, and temporal features proposed by ADJ, we further propose three effective features, including centrality, role, and topic features, to capture how users interact in the history and how different topics affect the success of requests. Experiments conducted on the requests in the “Random Acts of Pizza” community of Reddit.com show GPRS can lead to around 0.81 and 0.68 AUC scores using sufficient and limited training data respectively, which significantly outperform ADJ by 0.14 and 0.08 respectively.