**5. Conclusions**

In this paper, we proposed a method of constructing a relation matrix by considering the correlation and sparsity of paired samples. We then added the characteristics of a GCN, which aggregates similar samples, to finally obtain the probability of occurrence of each label. Experimental results on six datasets showed that our proposed method can deliver superior performance in comparison with eleven baselines. Our future work will include designing a general framework that can reduce the use of memory and increase the efficiency of a GCN and extending this framework to unsupervised learning.
