This document presents a new multi-label relational neighbor classification method called SCRN that uses social context features to improve classification performance on multi-label networked datasets. SCRN extracts social context features using edge clustering to represent potential group memberships. It then calculates class propagation probabilities and predicts labels using collective inference. Experimental results on DBLP, IMDb and YouTube datasets show SCRN significantly outperforms other methods in terms of various evaluation metrics like Micro-F1, Macro-F1 and Hamming Loss.