MI-GNN: Multi-Interaction GNN for Various Weak Information Learning on Graphs

Graph Neural Networks (GNNs) have achieved significant success in graph-related tasks, particularly in scenarios involving graphs with comprehensive information. Nevertheless, the performance of GNNs is often hindered in real-world applications due to the presence of incomplete graph data, characterized by fragmented structures, missing features, and insufficient labels. Previous research in this field has predominantly concentrated on augmenting one specific individual aspect of such weak information, thereby neglecting the holistic graph learning challenge. In this paper, we introduce a new framework, named MI-GNN, standing for Multi-Interaction Graph Neural Network, which is innovatively designed to facilitate message passing across graphs while adeptly harnessing the interplay and synergies among various types of information in graph learning tasks. This approach holistically integrates graph structures, features, and labels into a cohesive learning model, thus significantly enhancing graph learning capabilities for graphs with incomplete data. Furthermore, to achieve consistency in these enhanced tasks, our framework integrates multi-task learning to address the challenges of Graph Learning with Weak Information (GLWI). We adopt an adaptive alignment strategy, dynamically assigning weights to each learning task in every iteration. Empirical evaluation of eight publicly accessible datasets demonstrates that our MI-GNN framework achieves state-of-the-art performance in handling a range of weak information scenarios in graph learning.