Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked examples, i.e. examples sharing pieces of information (such as vertices or edges). We propose an efficient weighting method for learning from networked examples and show a sample error bound which is better than previous work.