Meta-Path Learning for Multi-relational Graph Neural Networks
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths. However, the former approach faces challenges in the presence of many relations (e.g., knowledge graphs), while the latter requires substantial domain expertise to identify relevant meta-paths. In this work we propose a novel approach to learn meta-paths and meta-path GNNs that are highly accurate based on a small number of informative meta-paths. Key element of our approach is a scoring function for measuring the potential informativeness of a relation in the incremental construction of the meta-path. Our experimental evaluation shows that the approach manages to correctly identify relevant meta-paths even with a large number of relations, and substantially outperforms existing multi-relational GNNs on synthetic and real-world experiments.
Code (1)
Tasks
InformativenessKnowledge GraphsSimilar Papers 제목 키워드 기반
MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection
Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a graph in which each row is a node and each foreign-key relation is an …
Graph Neural NetworkA Self-Explainable Heterogeneous GNN for Relational Deep Learning
Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technology for predictive tasks. However, existin…
Deep LearningGraph Neural NetworkMeta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
Existing message passing neural networks for heterogeneous graphs rely on the concepts of meta-paths or meta-graphs due to the intrinsic nature of heterogeneous graphs. However, the meta-paths and meta-graphs need to be …
Contrastive LearningGraph LearningNode ClusteringDecoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
The widespread use of plastics and their persistence in the environment have led to the accumulation of micro- and nano-plastics across air, water, and soil, posing serious health risks including respiratory, gastrointes…
AlertStar: Path-Aware Alert Prediction on Hyper-Relational Knowledge Graphs
Cyber-attacks continue to grow in scale and sophistication, yet existing network intrusion detection approaches lack the semantic depth required for path reasoning over attacker-victim interactions. We address this by fi…
Network Intrusion DetectionKnowledge Graph CompletionKnowledge Graphs