Towards effective graph representations by leveraging geometric concepts - Lee, See Hian (Nanyang Technological University, 2024)
The field of graph representation learning (GRL) is dedicated to the task of encoding graph-structured data into low-dimensional vectors, often referred to as embeddings. Obtaining effective representations for various graph-related tasks, such as node classification and link prediction, hinges on effectively leveraging both the attributes and structural aspects of the graph data. A prominent approach for acquiring these graph representations involves the utilization of Graph Neural Networks (GNNs), a specialized class of neural networks designed for learning from graph data. Existing GNNs have limitations that can be mitigated by tailoring refinements based on the graph data's characteristics, enabling a more effective capture of graph intricacies. These improvements stand to benefit various applications, such as recommendation systems and social networks, as the graph structure often unveils valuable latent information.