In this paper, we introduce $\texttt{TopoSRL}$, a novel self-supervised learning (SSL) method for simplicial complexes to effectively capture higher-order interactions and preserve topology in the learned representations.

In this paper, we introduce \texttt{TopoSRL}, a novel self-supervised learning (SSL) method for simplicial complexes to effectively capture higher-order interactions and preserve topology in the learned representations. We propose a new simplicial augmentation technique that generates two views of the simplicial complex that enriches the representations while being efficient. Next, we propose a new simplicial contrastive loss function that contrasts the generated simplices to preserve local and global information present in the simplicial complexes. Extensive experimental results demonstrate the superior performance of \texttt{TopoSRL} compared to state-of-the-art graph SSL techniques and supervised simplicial neural models across various datasets corroborating the efficacy of \texttt{TopoSRL} in processing simplicial complex data in a self-supervised setting.