# HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification

arXiv: https://arxiv.org/abs/2305.15182 · HTML: https://mlsanity.com/paper/2305.15182/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Structural Entropy Guided Graph Hierarchical Pooling](https://mlsanity.com/paper/2206.13510/) (2022)
- [Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification](https://mlsanity.com/paper/2203.03825/) (2022)
- [Hierarchical Text Classification with Reinforced Label Assignment](https://mlsanity.com/paper/1908.10419/) (2019)
- [Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification](https://mlsanity.com/paper/1909.04176/) (2019)
- [Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification](https://mlsanity.com/paper/1906.04898/) (2019)
- [HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification](https://mlsanity.com/paper/2204.13413/) (2022)
- [Hyperbolic Interaction Model For Hierarchical Multi-Label Classification](https://mlsanity.com/paper/1905.10802/) (2019)
- [HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization](https://mlsanity.com/paper/2104.05220/) (2021)
- [Exploiting Global and Local Hierarchies for Hierarchical Text Classification](https://mlsanity.com/paper/2205.02613/) (2022)
- [A Simple yet Effective Method for Graph Classification](https://mlsanity.com/paper/2206.02404/) (2022)
- [Efficient strategies for hierarchical text classification: External knowledge and auxiliary tasks](https://mlsanity.com/paper/2005.02473/) (2020)
- [SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning](https://mlsanity.com/paper/2305.04501/) (2023)
- [Hierarchical information matters: Text classification via tree based graph neural network](https://mlsanity.com/paper/2110.02047/) (2021)

## Built on by (most cited first, 17 of 17)

- [DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning](https://mlsanity.com/paper/2510.17489/) (2025)
- [LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification](https://mlsanity.com/paper/2403.16504/) (2024)
- [Revisiting Hierarchical Text Classification: Inference and Metrics](https://mlsanity.com/paper/2410.01305/) (2024)
- [Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification](https://mlsanity.com/paper/2310.05128/) (2023)
- [NC-NCD: Novel Class Discovery for Node Classification](https://mlsanity.com/paper/2407.17816/) (2024)
- [Structural-Entropy-Based Sample Selection for Efficient and Effective Learning](https://mlsanity.com/paper/2410.02268/) (2024)
- [UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis](https://mlsanity.com/paper/2311.01775/) (2023)
- [Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification](https://mlsanity.com/paper/2402.18825/) (2024)
- [Rumor Detection on Social Media with Temporal Propagation Structure Optimization](https://mlsanity.com/paper/2412.08316/) (2024)
- [From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classification](https://mlsanity.com/paper/2411.14252/) (2024)
- [LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning](https://mlsanity.com/paper/2412.16963/) (2024)
- [HiLight: A Hierarchy-aware Light Global Model with Hierarchical Local ConTrastive Learning](https://mlsanity.com/paper/2408.05786/) (2024)
- [Uncovering Capabilities of Model Pruning in Graph Contrastive Learning](https://mlsanity.com/paper/2410.20356/) (2024)
- [Adaptive Data-Resilient Multi-Modal Hierarchical Multi-Label Book Genre Identification](https://mlsanity.com/paper/2505.03839/) (2025)
- [Hierarchical Book Organization for Learning-Resource Discovery using Dual-Path Graph Convolutions](https://mlsanity.com/paper/2512.21076/) (2025)
- [Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance](https://mlsanity.com/paper/2601.08418/) (2026)
- [Mastering the Minority: An Uncertainty-guided Multi-Expert Framework for Challenging-tailed Sequence Learning](https://mlsanity.com/paper/2603.15708/) (2026)

## Abstract (arXiv)

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. Under such observation, we tend to investigate the feasibility of a memory-friendly model with strong generalization capability that could boost the performance of HTC without prior statistics or label semantics. In this paper, we propose Hierarchy-aware Tree Isomorphism Network (HiTIN) to enhance the text representations with only syntactic information of the label hierarchy. Specifically, we convert the label hierarchy into an unweighted tree structure, termed coding tree, with the guidance of structural entropy. Then we design a structure encoder to incorporate hierarchy-aware information in the coding tree into text representations. Besides the text encoder, HiTIN only contains a few multi-layer perceptions and linear transformations, which greatly saves memory. We conduct experiments on three commonly used datasets and the results demonstrate that HiTIN could achieve better test performance and less memory consumption than state-of-the-art (SOTA) methods.