HITS-GNN: A Simplified Propagation Scheme for Graph Neural Networks

In recent years, Graph Neural Networks (GNNs) have gained popularity for solving a wide range of problems, primarily due to the proliferation of graph data across various domains. GNNs offer expressive power but are computationally expensive at the same time. Some studies have suggested that altering their traditional message passing mechanism with Personalized PageRank as a propagation scheme reduces the computational complexity, improves performance, and optimizes scalability in semi-supervised learning problems. This paper presents a propagation mechanism based on the Hyperlink-Induced Topic Search (HITS) algorithm. The HITS-based approach propagates information in a graph by using a recursive update of authority and hub scores. Using this terminology, Personalized PageRank based propagation considers only in-links, thus, embraces the concept of authority (in-links) scores while ignoring the important concept of hub (out-links), which leads to trailing down some valuable information. According to our approach, a Multi-Layer Perceptron (MLP) is applied in combination with a HITS-based propagation algorithm to separate node prediction and propagation. Experimental results demonstrate that the proposed method outperforms baseline methods on graph benchmark datasets with a significant margin for semi-supervised node classification.