Health and Behavior
DEEP LEARNING FOR UNSTRUCTURED DATA ANALYTICS AND MINING
REU Scholar: Richard Gao
REU Scholar Home Institution: Rice University
REU Mentor: Xingquan Zhu, Ph.D.
Graph Learning For Network Data
Many datasets naturally lend themselves to be structured as graphs. Leveraging their topological information allows Graph Neural Networks (GNN) to outperform their non-graph based counterparts. However, most real world graph datasets are heterogeneous and cannot be processed by traditional GNNs due to inconsistent node and edge typing. In this presentation, we introduce a homogenizing pipeline that will allow us to modify these data sets to be compatible with current GNN technology. By choosing a specific metapath and nodetype, we can transform heterogeneous, single-labeled graph data into homogeneous, multi-labeled data that we can pass through traditional GNN structures. The multi-labeled nature of the transformed data requires us to adapt traditional, single-label classification models to scale to the multiple-labels, or create new, multi-label specific models. Then, by evaluating these different GNN architectures on the transformed heterogeneous graphs and comparing their performances, we can find the optimal architecture for the multi-label node classification task in the context of heterogeneous graphs.