Cross-level graph contrastive learning for community value prediction

Community Value Prediction (CVP) is an important emerging task in the field of social commerce, which aims to predict the community values. However, due to the complex structure of communities and individuals, previous graph machine learning methods have struggled to adequately address this task. This study endeavors to bridge this gap by introducing a cross-level graph contrastive learning method called Cross-level Community Contrastive Learning (CCCL) to handle such subgraph-level tasks. Specifically, we generate two views that describe different levels of social connections, the augmented node-level graph and the community-level graph that is produced by graph coarsening. Subsequently, CCCL captures the mutual information between the two views through a cross-view contrastive loss. The learned embeddings utilize community and node information at various levels, making them capable of handling subgraph-level regression problems. To the best of our knowledge, CCCL is the first graph contrastive learning method that addresses the CVP problem. We theoretically show that CCCL maximizes a lower bound of the mutual information shared between node-view and community-view representations. Experimental results demonstrate that our proposed approach is highly effective for the CVP task, outperforming both end-to-end and self-supervised baselines. Furthermore, our model also exhibits robust resistance to edge perturbation attacks.