Sentiment analysis, also known as opinion mining

Graph Neural Networks (GNNs) have emerged as a powerful paradigm for analyzing structured data, offering unique advantages in capturing relationships and dependencies between data points represented as nodes in a graph. GNNs excel at exploiting the rich relational information inherent in graph structures

Relational Graph Convolutional Networks (RGCNs) address this by using different types of edges to capture arXiv:2404.13079v1 [cs.CL] 16 Apr 2024 different relationships

A heterogeneous graph is a more flexible way to represent networks where data can come in various forms [20]. Unlike a homogeneous graph, a heterogeneous graph allows for different types of nodes and edges.