self-weighting-mechanism

This paper proposes a unified contrastive framework for learning graph representations across multiple abstraction levels (node, proximity, cluster, graph) with a parameter-free self-weighting mechanism that adaptively assigns weights to similarity scores, outperforming state-of-the-art on downstream tasks like classification, clustering, and link prediction.