# Semi-supervised learning of hierarchical representations of molecules using neural message passing

arXiv: https://arxiv.org/abs/1711.10168 · HTML: https://mlsanity.com/paper/1711.10168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network](https://mlsanity.com/paper/2104.13561/) (2021)
- [A dictionary learning framework for graphs via filters and optimal transport](https://mlsanity.com/paper/2609.05919/) (2026)

## Abstract (arXiv)

With the rapid increase of compound databases available in medicinal and material science, there is a growing need for learning representations of molecules in a semi-supervised manner. In this paper, we propose an unsupervised hierarchical feature extraction algorithm for molecules (or more generally, graph-structured objects with fixed number of types of nodes and edges), which is applicable to both unsupervised and semi-supervised tasks. Our method extends recently proposed Paragraph Vector algorithm and incorporates neural message passing to obtain hierarchical representations of subgraphs. We applied our method to an unsupervised task and demonstrated that it outperforms existing proposed methods in several benchmark datasets. We also experimentally showed that semi-supervised tasks enhanced predictive performance compared with supervised ones with labeled molecules only.