Modeling real-world events as structured graphs is essential for advancing research in information retrieval, digital history, and narrative analysis. In this paper, we propose StoryNetworks, a novel dataset that transforms short event texts into annotated event networks. We curated 5,204 events from the Wikipedia Current Events Portal spanning 2016 and 2017, and manually annotated 2,494 directed dependencies between them. By bridging unstructured textual data with graph-based event modeling, StoryNetworks offers a valuable resource for computational social science and digital humanities. In addition, as creating dependency graph from short texts is a challenging task, this dataset should be useful for designing new models in event evolution modeling, narrative structure analysis, and information diffusion to obtain better accuracy. The dataset is publicly available at https://github.com/sumilab/dataset .