Meta-Path Learning for Multi-relational Graph Neural Networks

Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger

Publikation: Konferencebidrag uden forlag/tidsskriftPaper uden forlag/tidsskriftForskningpeer review


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.
Antal sider17
StatusUdgivet - 2023
BegivenhedThe Second Learning on Graphs Conference - Online
Varighed: 27 nov. 202330 nov. 2023
Konferencens nummer: 2


KonferenceThe Second Learning on Graphs Conference


Dyk ned i forskningsemnerne om 'Meta-Path Learning for Multi-relational Graph Neural Networks'. Sammen danner de et unikt fingeraftryk.