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arXiv · 1810.12503

Unsupervised Meta-path Reduction on Heterogeneous Information Networks

Xiaokai Wei·Zhiwei Liu·Lichao Sun·Philip S. Yu
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Abstract

Heterogeneous Information Network (HIN) has attracted much attention due to its wide applicability in a variety of data mining tasks, especially for tasks with multi-typed objects. A potentially large number of meta-paths can be extracted from the heterogeneous networks, providing abundant semantic knowledge. Though a variety of meta-paths can be defined, too many meta-paths are redundant. Reduction on the number of meta-paths can enhance the effectiveness since some redundant meta-paths provide interferential linkage to the task. Moreover, the reduced meta-paths can reflect the characteristic of the heterogeneous network. Previous endeavors try to reduce the number of meta-paths under the guidance of supervision information. Nevertheless, supervised information is expensive and may not always be available. In this paper, we propose a novel algorithm, SPMR (Semantic Preserving Meta-path Reduction), to reduce a set of pre-defined meta-paths in an unsupervised setting. The proposed method is able to evaluate a set of meta-paths to maximally preserve the semantics of original meta-paths after reduction. Experimental results show that SPMR can select a succinct subset of meta-paths which can achieve comparable or even better performance with fewer meta-paths.

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Xiaokai Wei, Zhiwei Liu, Lichao Sun, Philip S. Yu. 2018-10-30. Unsupervised Meta-path Reduction on Heterogeneous Information Networks. https://arxiv.org/abs/1810.12503

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