# Hyperbolic Interaction Model For Hierarchical Multi-Label Classification

arXiv: https://arxiv.org/abs/1905.10802 · HTML: https://mlsanity.com/paper/1905.10802/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 30 of 30)

- [Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification](https://mlsanity.com/paper/2203.03825/) (2022)
- [HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification](https://mlsanity.com/paper/2204.13413/) (2022)
- [Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification](https://mlsanity.com/paper/2201.03194/) (2022)
- [HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification](https://mlsanity.com/paper/2305.15182/) (2023)
- [HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning](https://mlsanity.com/paper/2109.10500/) (2021)
- [Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification](https://mlsanity.com/paper/2306.14822/) (2023)
- [Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification](https://mlsanity.com/paper/2109.06661/) (2021)
- [Geometric Relational Embeddings: A Survey](https://mlsanity.com/paper/2304.11949/) (2023)
- [Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification](https://mlsanity.com/paper/2101.04997/) (2021)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2211.13873/) (2022)
- [Revisiting Hierarchical Text Classification: Inference and Metrics](https://mlsanity.com/paper/2410.01305/) (2024)
- [Recent Advances in Hierarchical Multi-label Text Classification: A Survey](https://mlsanity.com/paper/2307.16265/) (2023)
- [HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation](https://mlsanity.com/paper/2107.05366/) (2021)
- [Classification of Goods Using Text Descriptions With Sentences Retrieval](https://mlsanity.com/paper/2111.01663/) (2021)
- [All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)](https://mlsanity.com/paper/2206.08653/) (2022)
- [Learning Label Hierarchy with Supervised Contrastive Learning](https://mlsanity.com/paper/2402.00232/) (2024)
- [Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification](https://mlsanity.com/paper/2402.18825/) (2024)
- [Cross-lingual Word Embeddings in Hyperbolic Space](https://mlsanity.com/paper/2205.01907/) (2022)
- [Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces](https://mlsanity.com/paper/2209.05635/) (2022)
- [Explainable Product Classification for Customs](https://mlsanity.com/paper/2311.10922/) (2023)
- [Hierarchy-Consistent Learning and Adaptive Loss Balancing for Hierarchical Multi-Label Classification](https://mlsanity.com/paper/2508.13452/) (2025)
- [TagRec++: Hierarchical Label Aware Attention Network for Question Categorization](https://mlsanity.com/paper/2208.05152/) (2022)
- [LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning](https://mlsanity.com/paper/2412.16963/) (2024)
- [Research on Domain Information Mining and Theme Evolution of Scientific Papers](https://mlsanity.com/paper/2204.08476/) (2022)
- [Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification](https://mlsanity.com/paper/2211.10685/) (2022)
- [Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels](https://mlsanity.com/paper/2302.14695/) (2023)
- [Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls](https://mlsanity.com/paper/2308.06895/) (2023)
- [Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis](https://mlsanity.com/paper/2510.01118/) (2025)
- [Hyperbolic Structured Classification for Robust Single Positive Multi-label Learning](https://mlsanity.com/paper/2510.15296/) (2025)
- [Geometry-Aware Hyperbolic Residual-Quantized Variational Autoencoders](https://mlsanity.com/paper/2609.26342/) (2026)

## Abstract (arXiv)

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available.