Multi-label classification is widely used in genetic classification

drug discovery and text classification.The existing multi-label classification algorithms usually select nodes randomly from the network as their training set.However

during multi-label classification

different nodes have different effects.Given the number of nodes in the training set

a different training sub-set can lead to different classification accuracy.Hence

we introduce the concept of seed nodes

the classification procedure starts from the seed nodes

and after continuous reasoning

the labels of other nodes are inferred in the network.We propose an SHDA algorithm (Nodes Selection of High Degree from Each Affiliation) in which the nodes of high degrees from each affiliation belonging to the network are selected and merged

and after processing

the seed nodes are obtained.Experiments on several real-world datasets demonstrate that taking seed nodes as the training set to classify multi-labeled data can improve the classification performance.