# Application of Label Correlation in Multi-Label Classification: A Survey
## 3 Label Correlation Mining Methods
### Label Graph-Based Methods  
Using graph structures to model relationships between labels is an effective method [37; 38]. When constructing label graph structures, each label is considered as a vertex in the graph, and the relationships between labels are represented by edges. To quantify the strength of these relationships, the edges in the graph are typically assigned corresponding weights based on label co-occurrence frequency, conditional probability, and other related information, reflecting the correlations between labels. Furthermore, for a more accurate exploration of label correlations, it is necessary to consider the complex relationships between labels. This includes analyzing label co-occurrence or exclusion relationships present in specific datasets or scenarios [28; 39], which can reveal the latent interdependence or exclusionary characteristics between labels. Additionally, labels may also be organized in a hierarchical tree structure, forming hierarchical relationships [40]. This hierarchical relationship aids in describing the logical and semantic connections between labels. Graph structures, as a flexible and powerful modeling tool, can effectively model not only single types of label relationships but also integrate various types of label relationships. In the early stages of research, researchers primarily focused on exploring the hierarchical relationships between labels and used tree structures or more complex directed acyclic graphs to reveal these relationships. In the process of constructing hierarchical structures for labels, researchers often relied on external prior knowledge, such as various knowledge graphs and common-sense semantic networks, to provide clues about the hierarchical relationships between labels. For example, commonly used lexical semantic networks like WordNet [41] and ConceptNet [42] provide rich hierarchical relationship information for MLC tasks. [PERSON] et al. [43] extracted synonym sets corresponding to category labels from WordNet and then utilized WordNet's hypernym-hyponym relationships and part-whole relationships to provide two types of semantic hierarchical relationships, thus constructing a comprehensive label semantic hierarchy graph that considers multiple relationships. Based on this label hierarchy graph, they designed a semantic hierarchy classifier for visual object detection, successfully integrating the relevance of image labels in visual recognition tasks. Later, [PERSON] et al. [44] proposed a new representation structure based on WordNet, called HEX graph, which simultaneously characterizes three types of relationships between labels: exclusion, co-occurrence, and hierarchy. As shown in Figure 3, the labels \"Dog\" and \"Cat\" indicate a mutually exclusive relationship, meaning that these two categories cannot simultaneously apply to the same object instance. The overlapping area between the labels \"Husky\" and \"Puppy\" represents a co-occurrence relationship, indicating that a single \"Puppy\" can be classified as both a \"Husky\" and a \"Dog\". Additionally, the label \"Dog\", as a parent class of \"Husky\" and \"Puppy\", illustrates a hierarchical relationship, where one label encompasses another. This work was the first to jointly model label hierarchy and label exclusion relationships. [PERSON] et al. [37] further extended the HEX graph to allow for the introduction of soft or probabilistic relationships between labels. Additionally, [PERSON] et al. [45] also extracted structured label relationships by parsing relationships in WordNet, including positive and negative label correlations, and then generated a label relationship graph to enhance the inference process of neural networks. The commonality among the aforementioned works is the use of WordNet as an external knowledge source for label relationships, leveraging this prior knowledge to construct a hierarchical structure graph of labels and guide learning tasks.