HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs

arXiv:2404.00816v1 Announce Type: new
Abstract: Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings in such graphs is a critical problem in graph machine learning. However, existing solutions for this problem fail to scale to large heterogeneous graphs due to their high computational complexity. To address this issue, we propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) - a generic methodology that allows contemporary …

abstract applications arxiv cs.ai cs.lg embeddings framework graph graph representation graphs however machine machine learning relationships representation representation learning scale solutions type types world