# Tag2Vec: Learning Tag Representations in Tag Networks

arXiv: https://arxiv.org/abs/1905.03041 · HTML: https://mlsanity.com/paper/1905.03041/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 8 of 8)

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- [Neuron with Steady Response Leads to Better Generalization](https://mlsanity.com/paper/2111.15414/) (2021)
- [HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction](https://mlsanity.com/paper/2202.12713/) (2022)
- [Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search](https://mlsanity.com/paper/2107.04773/) (2021)
- [DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://mlsanity.com/paper/2210.12402/) (2022)

## Abstract (arXiv)

Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks. In real networks, nodes may have multiple tags but existing methods ignore the abundant semantic and hierarchical information of tags. This information is useful to many network applications and usually very stable. In this paper, we propose a tag representation learning model, Tag2Vec, which mixes nodes and tags into a hybrid network. Firstly, for tag networks, we define semantic distance as the proximity between tags and design a novel strategy, parameterized random walk, to generate context with semantic and hierarchical information of tags adaptively. Then, we propose hyperbolic Skip-gram model to express the complex hierarchical structure better with lower output dimensions. We evaluate our model on the NBER U.S. patent dataset and WordNet dataset. The results show that our model can learn tag representations with rich semantic information and it outperforms other baselines.