Subevent Relation Extraction (SRE) is a task in Information Extraction that aims to recognize spatial and temporal containment relations be-tween event mentions in text. Recent methods have utilized pre-trained language models to represent input texts for SRE. However, a key issue in existing SRE methods is the employ-ment of sequential order of words in texts to feed into representation learning methods, thus unable to explicitly focus on important context words and their interactions to enhance repre-sentations. In this work, we introduce a new method for SRE that learns to induce effective graph structures for input texts to boost repre-sentation learning. Our method features a word alignment framework with dependency paths and optimal transport to identify important con-text words to form effective graph structures for SRE. In addition, to enable SRE research on non-English languages, we present a new multilingual SRE dataset for five typologically different languages. Extensive experiments re-veal the state-of-the-art performance for our method on different datasets and languages.