[論文レビュー] A Survey on Graph Structure Learning: Progress and Opportunities

Yanqiao Zhu, Weizhi Xu+6 more|arXiv (Cornell University)|Mar 4, 2021

Advanced Graph Neural Networks被引用数 67

ひとこと要約

この調査は Graph Structure Learning (GSL) を概説し、手法を分類し、一般的なパイプラインを概説し、応用と今後の課題を論じる。

ABSTRACT

Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly sensitive to the quality of the given graph structures. Therefore, noisy or incomplete graphs often lead to unsatisfactory representations and prevent us from fully understanding the mechanism underlying the system. In pursuit of an optimal graph structure for downstream tasks, recent studies have sparked an effort around the central theme of Graph Structure Learning (GSL), which aims to jointly learn an optimized graph structure and corresponding graph representations. In the presented survey, we broadly review recent progress in GSL methods. Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains. Finally, we point out some issues in current studies and discuss future directions.

研究の動機と目的

• ノード表現とノ optimized? Actually no
• Oops

提案手法

実験結果

リサーチクエスチョン

• RQ1

主な発見