Journal Article

A Data-centric graph neural network for node classification of heterophilic networks

Gao, Wenlian, Xue, Yanfeng, Jin, Zhen

2024

Overview

In the real world, numerous heterophilic networks effectively model the tendency of similar entities to repel each other and dissimilar entities to be attracted to each other within complex systems. Concerning the node classification problem in heterophilic networks, a plethora of heterophilic Graph Neural Networks (GNNs) have emerged. However, these GNNs demand extensive hyperparameter tuning, activation function selection, parameter initialization, and other configuration settings, particularly when dealing with diverse heterophilic networks and resource constraints. This situation raises a fundamental question: Can a method be designed to directly preprocess heterophilic networks and then leverage the trained models in network representation learning systems? In this paper, we propose a novel approach to transform heterophilic network structures. Specifically, we train an edge classifier and subsequently employ this edge classifier to transform a heterophilic network into its corresponding homophilic counterpart. Finally, we conduct experiments on heterophilic network datasets with variable sizes, demonstrating the effectiveness of our approach. The code and datasets are publicly available at https://github.com/xueyanfeng/D_c_GNNs .

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Artificial Intelligence / Classification / Complex Systems / Computational Intelligence / Control / Datasets / Design / Effectiveness / Engineering / Graph neural networks / Machine learning / Mechatronics / Neighborhoods / Neural networks / Original Article / Pattern Recognition / Robotics / Systems Biology