# A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks

arXiv: https://arxiv.org/abs/2203.12569 · HTML: https://mlsanity.com/paper/2203.12569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification](https://mlsanity.com/paper/1802.08888/) (2018)
- [Solving for multi-class: a survey and synthesis](https://mlsanity.com/paper/1809.05929/) (2018)

## Built on by (most cited first, 3 of 3)

- [Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification](https://mlsanity.com/paper/2203.13551/) (2022)
- [Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification](https://mlsanity.com/paper/2207.06237/) (2022)
- [Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation](https://mlsanity.com/paper/2404.06124/) (2024)

## Abstract (arXiv)

Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification (HMC), where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica, a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.