Neighbor Contrastive Learning with Weakened Consensus Graph for Deep Multi-View Clustering

In recent years, deep multi-view clustering methods based on contrastive learning have gained significant attention. Most existing approaches treat the anchor and its cross-view representations as positive pairs, while the anchor and other samples are considered negative pairs. However, this pairwise assignment does not account for the higher similarity between the anchor and samples that are close, which should also be treated as positive pairs. To address this issue, we introduce topology-aware positive sampling: for each anchor, both its intra-view neighbors and cross-view consistent neighbors are selected as additional positive samples, which aligns contrastive learning with the homophily principle of clustering. Additionally, to obtain reliable neighbor relationships, most existing methods construct graphs from the original data or extracted features and average them to form a consensus graph. However, this approach overlooks the fact that views of varying quality should be assigned different weights, and unreliable connections within a view should be discarded. To overcome this, we design a global-guided weak connections suppression mechanism to weaken unreliable connections in the initial graph of each view, then apply weighted graph fusion to obtain a more accurate consensus graph. We also combine the view weights from graph fusion with the corresponding view's neighbor contrastive loss to enhance consistency between the two processes. Extensive experimental results demonstrate the superiority of our proposed method.