(untitled) (Contemporary Mathematics, 2025)
Graph Neural Networks (GNNs) have achieved notable success in graph classification tasks, such as chemical molecular graph classification in cheminformatics and drug design, owing to their ability to effectively capture structural features of molecular graphs. However, conventional GNNs still face challenges in comprehensively characterizing complex node interactions, particularly in discerning distinct node roles and their intricate interplay. To address this limitation, we propose a novel Hyperlink-Induced Topic Search (HITS)-inspired Interactive Graph ATtention network (HITS-InterGAT) for graph classification. This model employs an interactive attention mechanism to capture nuanced node interactions within molecular graphs. Specifically, we integrate the HITS algorithm to compute node authority and hub features, followed by an interaction graph attention mechanism to model feature-level relationships. Extensive experiments on multiple chemical and molecular datasets demonstrate that HITS-InterGAT significantly outperforms existing baselines in both classification accuracy and robustness. Our findings underscore the substantial potential of combining interactive attention mechanisms with the HITS algorithm for advancing chemical molecular graph classification.