Triangle count estimation and label prediction over uncertain streaming graphs

Abstract

This thesis aims to integrate the notions of uncertainty with graph stream process- ing, presenting probabilistic models to enhance real-time analytical capabilities in graph database systems. These systems are crucial for managing interconnected data in various domains, such as social networks, traffic networks, and genomic databases, where data often contains incomplete or probabilistic connections that complicate processing and analysis. We develop and validate two main methodologies: a martingale-based approach for approximating triangle counts in edge uncertain streaming graphs and a Graph Neural Network (GNN)-based method for dynamic label prediction in attribute uncertain stream- ing graphs. Both methods demonstrate robust performance in handling dynamic and uncertain data, thus opening new avenues for future research in expanding the scope of graph-based analytics. This work lays the groundwork for future developments in uncer- tain graph processing, suggesting pathways to refine these approaches and explore new applications in dynamic environments.