Subgraph extraction algorithm for large scale heterogeneous graph

• Main Author: Wu, Eric JiaQing
• Other Authors: Luo Siqiang
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Computer and Information Science; Computer science
• Online Access: https://hdl.handle.net/10356/175480
• Institution: Nanyang Technological University

Large-scale heterogeneous graphs, containing different types of nodes and edges, bring new challenges to the efficiency and scalability of graph representation learning and Graph Neural Network-based algorithms. For some graph learning tasks, such as inductive graph reasoning, pre-training on whole graph data suffers from serious computational costs. Therefore, This project aims to propose an efficient subgraph extraction algorithm for large-scale heterogeneous graphs. The extracted subgraph contains a limited number of representative nodes and selected edge types, which can reflect the topological characteristics of the entire graph and is expected to be applied to various graph neural network models.