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This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi\u2010objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks\u2010based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances\u2010based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi\u2010Graph Representation Spatio\u2010Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi\u2010Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio\u2010Temporal Attention (STAtt) module is then proposed to extract spatio\u2010temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state\u2010of\u2010the\u2010art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.<\/jats:p>","DOI":"10.1002\/ett.5020","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T10:45:59Z","timestamp":1719917159000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010graph representation spatio\u2010temporal attention networks for traffic forecasting in the cinematic metaverse"],"prefix":"10.1002","volume":"35","author":[{"given":"Ke","family":"Li","sequence":"first","affiliation":[{"name":"School of Foreign Languages Xinxiang University Xinxiang China"}]},{"given":"Xiaoming","family":"He","sequence":"additional","affiliation":[{"name":"College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China"}]},{"given":"Yinqiu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Nanyang Technological University Singapore Singapore"}]},{"given":"Meng","family":"Chen","sequence":"additional","affiliation":[{"name":"Urban Transport Research Institute Shenzhen Urban Transport Planning Center Co., Ltd Shenzhen China"}]}],"member":"311","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"StephensonN.Snow Crash: a Novel.2007."},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","unstructured":"AnthesC Garc\u00eda\u2010Hern\u00e1ndezRJ WiedemannM et al.State of the art of virtual reality technology. 2016 IEEE aerospace conference. 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