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Specifically, we construct the spatial adjacency graph and spatial diffusion graph based on the different social-spatial dynamic relationships of terrorist attacks and determine the multi-scale period of time series data of terrorist attacks by using wavelet transform to model the temporal trend, period and closeness properties of terrorist attacks. The AST-MGCN mainly consists of spatial multi-graph convolution for extracting social-spatial features in multi-views and temporal convolution for capturing the transition rules. In addition, we also use the spatial\u2013temporal attention mechanism to effectively capture the most relevant spatial\u2013temporal dynamic information. Experiments on public datasets demonstrate that the proposed model outperforms the state-of-the-art baselines.<\/jats:p>","DOI":"10.1007\/s40747-023-01037-z","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T14:03:19Z","timestamp":1683208999000},"page":"6307-6328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Attention-based spatial\u2013temporal multi-graph convolutional networks for casualty prediction of terrorist attacks"],"prefix":"10.1007","volume":"9","author":[{"given":"Zhiwen","family":"Hou","sequence":"first","affiliation":[]},{"given":"Yuchen","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Fanliang","family":"Bu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"1037_CR1","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1177\/0263276402019004003","volume":"19","author":"U Beck","year":"2002","unstructured":"Beck U (2002) The terrorist threat: World Risk Society revisited. 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