# Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs

arXiv: https://arxiv.org/abs/2205.06783 · HTML: https://mlsanity.com/paper/2205.06783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 13 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive](https://mlsanity.com/paper/2105.07342/) (2021)
- [DGL-KE: Training Knowledge Graph Embeddings at Scale](https://mlsanity.com/paper/2004.08532/) (2020)
- [Molecular Contrastive Learning with Chemical Element Knowledge Graph](https://mlsanity.com/paper/2112.00544/) (2021)
- [DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science](https://mlsanity.com/paper/2106.14232/) (2021)
- [Incorporating Symbolic Domain Knowledge into Graph Neural Networks](https://mlsanity.com/paper/2010.13900/) (2020)
- [Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs](https://mlsanity.com/paper/1908.08612/) (2019)
- [Concept Representation Learning with Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2112.05677/) (2021)
- [Deep Learning for Molecular Graphs with Tiered Graph Autoencoders and Graph Prediction](https://mlsanity.com/paper/1910.11390/) (2019)
- [Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning](https://mlsanity.com/paper/2202.02601/) (2022)
- [Dual Embodied-Symbolic Concept Representations for Deep Learning](https://mlsanity.com/paper/2203.00600/) (2022)

## Abstract (arXiv)

Dual embodied-symbolic concept representations are the foundation for deep learning and symbolic AI integration. We discuss the use of dual embodied-symbolic concept representations for molecular graph representation learning, specifically with exemplar-based contrastive self-supervised learning (SSL). The embodied representations are learned from molecular graphs, and the symbolic representations are learned from the corresponding Chemical knowledge graph (KG). We use the Chemical KG to enhance molecular graphs with symbolic (semantic) knowledge and generate their augmented molecular graphs. We treat a molecular graph and its semantically augmented molecular graph as exemplars of the same semantic class, and use the pairs as positive pairs in exemplar-based contrastive SSL.