The main goal of job seekers is to identify job offers that match their profile. The same stands for human resource departments that aim to identify candidates, through their resumes, that match the recruiter's expectations. However, the number of job seekers and job offers is so important that none of human resource employees nor job seekers is able to go through all the resumes and offers manually. Recommender systems have emerged these last years with the goal to recommend job seekers and human resource departments, job offers and resumes respectively. One of the approaches adopted by the literature relies on the identification of content elements in the offers and resumes that contribute to perform matching. We propose to represent data under the form of graphs and approach this problem as a classification problem. We present DGL4C, a semi-supervised graph deep learning model, that learns the adequate representation from a graph and trains a classifier on this latent representation. Experiments are carried out on an open dataset of anonymous resumes. Results show that DGL4C significantly improves precision and accuracy of a traditional deep learning models, such as sBERT and confirm the pertinence of relying on a graph structure for the classification task in HR domain.