Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

TL;DR

This paper introduces a novel method to incorporate human expertise into graph neural networks, enhancing their ability to learn semantic information and improve performance across various domains.

Contribution

The paper proposes a new approach to embed human expertise logic into GNNs, enabling end-to-end learning of expert knowledge from data.

Findings

GNNs gradually learn human expertise in general domains

Incorporating expertise improves GNN performance

Experiments confirm the method's effectiveness on real-world data

Abstract

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the GNNs gradually learn human expertise in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. Hence, we propose a novel graph representation learning method to incorporate human expert knowledge into GNN models. The proposed method ensures that the GNN model can not only acquire the expertise held by human experts but also engage in end-to-end learning from datasets. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method.