UNAGI: Unified neighbor-aware graph neural network for multi-view clustering.

Multi-view graph refining-based clustering (MGRC) methods aim to facilitate the clustering of data via Graph Neural Networks (GNNs) by learning optimal graphs that reflect the underlying topology of the data. However, current MGRC approaches are limited by their disjoint two-stage process, where the graph structure is learned in the first stage before the GNN messages are propagated in the subsequent stage. Additionally, current approaches neglect the importance of cross-view structural consistency and semantic-level information and only consider intra-view embeddings.