Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction
This addresses link prediction for graph analysis, introducing a novel visual enhancement to MPNNs, though it builds incrementally on existing methods.
The paper tackles the problem of link prediction in graphs by enhancing message-passing neural networks (MPNNs) with visual perception, proposing Graph Vision Network (GVN) and its efficient variant (E-GVN). The result shows consistent improvements across seven datasets, achieving new state-of-the-art results.
Message-passing graph neural networks (MPNNs) and structural features (SFs) are cornerstones for the link prediction task. However, as a common and intuitive mode of understanding, the potential of visual perception has been overlooked in the MPNN community. For the first time, we equip MPNNs with vision structural awareness by proposing an effective framework called Graph Vision Network (GVN), along with a more efficient variant (E-GVN). Extensive empirical results demonstrate that with the proposed frameworks, GVN consistently benefits from the vision enhancement across seven link prediction datasets, including challenging large-scale graphs. Such improvements are compatible with existing state-of-the-art (SOTA) methods and GVNs achieve new SOTA results, thereby underscoring a promising novel direction for link prediction.