Multi-label classification in network environments is becoming a key area of data mining research as its applications are increasing dramatically.Relational classification models

which predict class labels of linked neighbors according to the ones of the given nodes

have been shown to outperform traditional multi-label classifiers.However

existing relational classification models neither make full use of neighbor information

nor predict the isolated nodes' labels

which are popularly existing in relational networks.In this paper

we present a multi-label relational classifier (MORN) that mines both second-order neighbors for non-isolated nodes and high-order neighbors for isolated nodes.MORN has been conducted on real datasets and it demonstrates that our proposed classifier outperforms existing relational classification models.