Ensemble Multi-Relational Graph Neural Networks

by   Yuling Wang, et al.

It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is able to flexibly remedy the weakness of GNNs. However, this optimization objective is only proved for GNNs with single-relational graph. Can we infer a new type of GNNs for multi-relational graphs by extending this optimization objective, so as to simultaneously solve the issues in previous multi-relational GNNs, e.g., over-parameterization? In this paper, we propose a novel ensemble multi-relational GNNs by designing an ensemble multi-relational (EMR) optimization objective. This EMR optimization objective is able to derive an iterative updating rule, which can be formalized as an ensemble message passing (EnMP) layer with multi-relations. We further analyze the nice properties of EnMP layer, e.g., the relationship with multi-relational personalized PageRank. Finally, a new multi-relational GNNs which well alleviate the over-smoothing and over-parameterization issues are proposed. Extensive experiments conducted on four benchmark datasets well demonstrate the effectiveness of the proposed model.


Graph Rewriting for Graph Neural Networks

Given graphs as input, Graph Neural Networks (GNNs) support the inferenc...

Interpreting and Unifying Graph Neural Networks with An Optimization Framework

Graph Neural Networks (GNNs) have received considerable attention on gra...

Graph Neural Networks with Generated Parameters for Relation Extraction

Recently, progress has been made towards improving relational reasoning ...

Relational State-Space Model for Stochastic Multi-Object Systems

Real-world dynamical systems often consist of multiple stochastic subsys...

A Dynamical Graph Prior for Relational Inference

Relational inference aims to identify interactions between parts of a dy...

Are Graph Neural Networks Really Helpful for Knowledge Graph Completion?

Knowledge graphs (KGs) facilitate a wide variety of applications due to ...

Estimating Aggregate Properties In Relational Networks With Unobserved Data

Aggregate network properties such as cluster cohesion and the number of ...

Please sign up or login with your details

Forgot password? Click here to reset