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Multi-task attributed graphical lasso and its application in fund classification

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Abstract

Sparse inverse covariance estimation, i.e., Graphical Lasso, reveals the underlying structure of graph for a set of variables on the basis of their observations. The estimated graphs can then facilitate a series of downstream tasks with graph mining techniques. Multi-task Graphical Lasso is designed for collectively estimating graphs sharing an identical set of variables, but it fails to contend with the situation when the tasks include different variables. In order to address this limitation, we propose Multi-task Attributed Graphical Lasso (MAGL) to learn graphs with observations and attributes jointly. Specifically, we introduce two concrete implementations, i.e., MAGL-LogDet and MAGL-HSIC, where the LogDet divergence and the Hilbert-Schmidt independence criterion are utilized respectively to explore latent relations between attributes of the variables and linkage structures among the variables. Experimental results show the effectiveness of MAGL-LogDet and MAGL-HSIC. We then apply MAGL to fund data and estimate stock graphs for each fund. We classify funds by using graph neural networks on the estimated graphs, and prove that we can benefit from MAGL in downstream tasks.

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Notes

  1. We suppress superscript k for simplicity.

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Funding

This work is funded in part by the Shanghai Science and Technology Development Fund No. 19511121204, and the National Natural Science Foundation of China Projects No. U1936213, No. U1636207.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

    Yao Zhang, Sijia Peng, Yun Xiong & Yangyong Zhu

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai, China

    Yun Xiong & Yangyong Zhu

  3. Worcester Polytechnic Institute, Worcester, MA, USA

    Xiangnan Kong & Xinyue Liu

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  1. Yao Zhang
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  2. Sijia Peng
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  3. Yun Xiong
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  4. Xiangnan Kong
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Correspondence to Yun Xiong.

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Availability of Data and Material

The data used in the paper is available at https://github.com/lunaaa95/lunahoho/tree/linux/data/Dataset.

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The codes are available at https://github.com/lunaaa95/lunahoho/tree/linux.

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Yao Zhang and Sijia Peng contribute equally to this work.

This article belongs to the Topical Collection: Special Issue on Graph Data Management, Mining, and Applications Guest Editors: Xin Wang, Rui Zhang, and Young-Koo Lee

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Zhang, Y., Peng, S., Xiong, Y. et al. Multi-task attributed graphical lasso and its application in fund classification. World Wide Web 25, 1425–1446 (2022). https://doi.org/10.1007/s11280-021-00959-3

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  • DOI: https://doi.org/10.1007/s11280-021-00959-3

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