**1. Introduction**

Traditional supervised learning deals with the analysis of single-label data, which means that samples are associated with a single label. However, in many real-world data mining applications, such as text classification [1, 2], scene classification [3, 4], crowd sensing/mining [5–11], and gene functional classification [12, 13], the samples are associated with more than one label. From this description, we understand that the challenge of the multilabel classification task is its potential output.

Basically, multilabel learning algorithms can be categorized into two different groups. 1) Problem transformation method. This method takes the multilabel problem and converts it into a single-label problem that can easily be classified using any classifier using the relationship between labels. 2) Adapted algorithm method. This method directly performs multilabel classification rather than transforming the problem into different subsets of problems, and most of these methods use the Euclidean distance between samples.

The main idea of this paper is to aggregate similar samples to obtain better results. To aggregate similar samples, we use the properties of graph neural networks (GNNs) [14]. The main contributions of this study are as follows:


The rest of this paper is arranged as follows. Section 2 shows the taxonomy of multilabel learning algorithms and describes their methods. Section 3 presents the details of our proposed method. Section 4 describes the multilabel datasets, evaluation metrics and experimental results, followed by the conclusions in Section 5.
