  Graph-based models and contrastive learning have emerged as prominent methods
in Collaborative Filtering (CF). While many existing models in CF incorporate
these methods in their design, there seems to be a limited depth of analysis
regarding the foundational principles behind them. This paper bridges graph
convolution, a pivotal element of graph-based models, with contrastive learning
through a theoretical framework. By examining the learning dynamics and
equilibrium of the contrastive loss, we offer a fresh lens to understand
contrastive learning via graph theory, emphasizing its capability to capture
high-order connectivity. Building on this analysis, we further show that the
graph convolutional layers often used in graph-based models are not essential
for high-order connectivity modeling and might contribute to the risk of
oversmoothing. Stemming from our findings, we introduce Simple Contrastive
Collaborative Filtering (SCCF), a simple and effective algorithm based on a
naive embedding model and a modified contrastive loss. The efficacy of the
algorithm is demonstrated through extensive experiments across four public
datasets. The experiment code is available at
\url{https://github.com/wu1hong/SCCF}. \end{abstract}
