# A Sparse Tensor Generator with Efficient Feature Extraction

arXiv: https://arxiv.org/abs/2405.04944 · HTML: https://mlsanity.com/paper/2405.04944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 7 of 7)

- [A Scalable Generative Graph Model with Community Structure](https://mlsanity.com/paper/1302.6636/) (2013)
- [Graph Generators: State of the Art and Open Challenges](https://mlsanity.com/paper/2001.07906/) (2020)
- [Load-Balanced Sparse MTTKRP on GPUs](https://mlsanity.com/paper/1904.03329/) (2019)
- [ALTO: Adaptive Linearized Storage of Sparse Tensors](https://mlsanity.com/paper/2102.10245/) (2021)
- [Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition](https://mlsanity.com/paper/1905.06246/) (2019)
- [Feature-based SpMV Performance Analysis on Contemporary Devices](https://mlsanity.com/paper/2302.04225/) (2023)
- [A Tensor Based Data Model for Polystore: An Application to Social Networks Data](https://mlsanity.com/paper/1806.09967/) (2018)

## Abstract (arXiv)

Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis. However, a major obstacle in sparse tensor research is the lack of large-scale sparse tensor datasets. Another challenge lies in analyzing sparse tensor features, which are essential not only for understanding the nonzero pattern but also for selecting the most suitable storage format, decomposition algorithm, and reordering methods. However, due to the large size of real-world tensors, even extracting these features can be computationally expensive without careful optimization. To address these limitations, we have developed a smart sparse tensor generator that replicates key characteristics of real sparse tensors. Additionally, we propose efficient methods for extracting a comprehensive set of sparse tensor features. The effectiveness of our generator is validated through the quality of extracted features and the performance of decomposition on the generated tensors. Both the sparse tensor feature extractor and the tensor generator are open source with all the artifacts available at https://github.com/sparcityeu/FeaTensor and https://github.com/sparcityeu/GenTensor, respectively.