MUlTI-LABEL-ClASSIFICATION

Multi-label classification tasks aim to predict multiple possible labels simultaneously for each input instance, rather than a single label. In the field of graph data processing, this task specifically focuses on multi-label classification of graph-structured data, achieving precise labeling of complex network relationships through efficient algorithms. The core objective is to optimize classification performance, achieving the highest accuracy and recall rates, thereby playing a significant role in areas such as bioinformatics, social network analysis, and social sciences.