Graph Rewiring For Improved AI Analysis

Graph Neural Networks are becoming a popular and effective tool due to their ability to capture both the labels of entities and their dyadic relations. However, the structure of the graph created can fundamentally affect the performance of AI. In this presentation, we will discuss how the variations in creating the graph from the raw data can affect the analysis, and how we can improve the graph structure
for improved accuracy of the analysis.