LCS graph kernel based on Wasserstein distance in longest common subsequence metric space

Jianming Huang, Zhongxi Fang, Hiroyuki Kasai*

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

For graph learning tasks, many existing methods utilize a message-passing mechanism where vertex features are updated iteratively by aggregation of neighbor information. This strategy provides an efficient means for graph features extraction, but obtained features after many iterations might contain too much information from other vertices, and tend to be similar to each other. This makes their representations less expressive. Learning graphs using paths, on the other hand, can be less adversely affected by this problem because it does not involve all vertex neighbors. However, most of them can only compare paths with the same length, which might engender information loss. To resolve this difficulty, we propose a new Graph Kernel based on a Longest Common Subsequence (LCS) similarity. Moreover, we found that the widely-used R-convolution framework is unsuitable for path-based Graph Kernel because a huge number of comparisons between dissimilar paths might deteriorate graph distances calculation. Therefore, we propose a novel metric space by exploiting the proposed LCS-based similarity, and compute a new Wasserstein-based graph distance in this metric space, which emphasizes more the comparison between similar paths. Furthermore, to reduce the computational cost, we propose an adjacent point merging operation to sparsify point clouds in the metric space.

本文言語English
論文番号108281
ジャーナルSignal Processing
189
DOI
出版ステータスPublished - 2021 12月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

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