Adversarial Cluster-Level and Global-Level Graph Contrastive Learning for node representation

Graph contrastive learning aims to learn informative and discriminative node representations for downstream tasks by maximizing the mutual information between representations of different augmentation views of the same node. However, according to the multi-view information bottleneck principle, redundant information in learned representations can negatively impact the performance of downstream tasks. To avoid this issue, we propose Adversarial Cluster-Level and Global-Level Graph Contrastive Learning (ACG-GCL) for learning minimal sufficient node representations. ACG-GCL is optimized alternately under an adversarial learning framework with a min-max objective. At the min step, ACG-GCL eliminates redundant information in both the graph structure and feature content of nodes while producing a new graph that provides multi-view information. At the max step, ACG-GCL seeks to preserve shared task-relevant information in the learned representation by maximizing the mutual information of node-cluster level and node-global level representations. We demonstrate the effectiveness of the proposed method on the tasks of node classification and node clustering tasks. Code is available at https://github.com/tangq123/ACG-GCL.(c) 2023 Elsevier B.V. All rights reserved.