Sparse tensor operations are gaining attention in emerging applications such
as social networks, deep learning, diagnosis, crime, and review analysis.
However, a major obstacle for research in sparse tensor operations is the
deficiency of a broad-scale sparse tensor dataset. Another challenge in sparse
tensor operations is examining the sparse tensor features, which are not only
important for revealing its nonzero pattern but also have a significant impact
on determining the best-suited storage format, the decomposition algorithm, and
the reordering methods. However, due to the large sizes of real tensors, even
extracting these features becomes costly without caution. To address these gaps
in the literature, we have developed a smart sparse tensor generator that
mimics the substantial features of real sparse tensors. Moreover, we propose
various methods for efficiently extracting an extensive set of features for
sparse tensors. The effectiveness of our generator is validated through the
quality of features and the performance of decomposition in the generated
tensors. Both the sparse tensor feature extractor and the tensor generator are
open source with all the artifacts available at
this https URL and this https URL,
respectively.

我们开发了一个智能稀疏张量生成器，模拟了真实稀疏张量的重要特征，并提出了多种有效的稀疏张量特征提取方法。