# CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling

arXiv: https://arxiv.org/abs/2110.05820 · HTML: https://mlsanity.com/paper/2110.05820/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [A Survey on Network Embedding](https://mlsanity.com/paper/1711.08752/) (2017)
- [PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks](https://mlsanity.com/paper/1508.00200/) (2015)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [Relation Structure-Aware Heterogeneous Information Network Embedding](https://mlsanity.com/paper/1905.08027/) (2019)
- [HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding](https://mlsanity.com/paper/1909.03228/) (2019)
- [Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations](https://mlsanity.com/paper/2001.08199/) (2020)
- [HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks](https://mlsanity.com/paper/1309.7393/) (2013)
- [Quantifying patterns of research interest evolution](https://mlsanity.com/paper/1709.03319/) (2017)
- [BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network](https://mlsanity.com/paper/1912.08925/) (2019)
- [Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood](https://mlsanity.com/paper/2008.07720/) (2020)
- [From syntactic structure to semantic relationship: hypernym extraction from definitions by recurrent neural networks using the part of speech information](https://mlsanity.com/paper/2012.03418/) (2020)

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

- [Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives](https://mlsanity.com/paper/2212.12715/) (2022)
- [CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/2112.03644/) (2021)
- [ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://mlsanity.com/paper/2310.04865/) (2023)

## Abstract (arXiv)

Heterogeneous information network (HIN) embedding aims to find the representations of nodes that preserve the proximity between entities of different nature. A family of approaches that are wildly adopted applies random walk to generate a sequence of heterogeneous context, from which the embedding is learned. However, due to the multipartite graph structure of HIN, hub nodes tend to be over-represented in the sampled sequence, giving rise to imbalanced samples of the network. Here we propose a new embedding method CoarSAS2hvec. The self-avoid short sequence sampling with the HIN coarsening procedure (CoarSAS) is utilized to better collect the rich information in HIN. An optimized loss function is used to improve the performance of the HIN structure embedding. CoarSAS2hvec outperforms nine other methods in two different tasks on four real-world data sets. The ablation study confirms that the samples collected by CoarSAS contain richer information of the network compared with those by other methods, which is characterized by a higher information entropy. Hence, the traditional loss function applied to samples by CoarSAS can also yield improved results. Our work addresses a limitation of the random-walk-based HIN embedding that has not been emphasized before, which can shed light on a range of problems in HIN analyses.