Generalizable Multi-Relational Graph Representation Learning: A Message Intervention Approach

Haoran Xin, Xinjiang Lu, Tong Xu, Dejing Dou, Hui Xiong

Keywords: Multi-Relational Graph, Causal Inference, Representation Learning, Graph Neural Network

TL;DR: A message intervention method for learning generalizable multi-relational graph representations

Abstract: With the edges associated with labels and directions, the so-called multi-relational graph possesses powerful expressiveness, which is beneficial to many applications. However, as the heterogeneity brought by the higher cardinality of edges and relations climbs up, more trivial relations are taken into account for the downstream task since they are often highly correlated to the target. As a result, with being forced to fit the non-causal relational patterns on the training set, the downstream model, like graph neural network (GNN), may suffer from poor generalizability on the testing set since the inference is mainly made according to misleading clues. In this paper, under the paradigm of graph convolution, we probe the multi-relational message passing process from the perspective of causality and then propose a Message Intervention method for learning generalizable muLtirElational gRaph representations, coined MILER. In particular, MILER first encodes the vertices and relations into embeddings with relational and directional awareness, then a message diverter is employed to split the original message flow into two flows of interest, i.e., the causal and trivial message flows. Afterward, the message intervention is carried out with the guidance of the backdoor adjustment rule. Extensive experiments on several knowledge graph benchmarks validate the effectiveness as well as the superior generalization ability of MILER.

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