{"type":"ScholarlyArticle","title":"MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions","authors":[{"name":"Nuo Xu","slug":"nuo-xu","given":"Nuo","family":"Xu","affiliation":"MOE NEKEY Lab , Xi'an Jiaotong University , China"},{"name":"Pinghui Wang","slug":"pinghui-wang","given":"Pinghui","family":"Wang","affiliation":"Shenzhen Research School , Xi'an Jiaotong University , China; MOE NEKEY Lab , Xi'an Jiaotong University , China"},{"name":"Long Chen","slug":"long-chen","given":"Long","family":"Chen","affiliation":"MOE NEKEY Lab , Xi'an Jiaotong University , China"},{"name":"Jing Tao","slug":"jing-tao","given":"Jing","family":"Tao","affiliation":"MOE NEKEY Lab , Xi'an Jiaotong University , China"},{"name":"Junzhou Zhao","slug":"junzhou-zhao","given":"Junzhou","family":"Zhao","affiliation":"MOE NEKEY Lab , Xi'an Jiaotong University , China"}],"abstract":"Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs, and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present {\\em MR-GNN}, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (LSTMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.","doi":"10.24963/ijcai.2019/551","url":"https://ijcai.org/proceedings/2019/551","pdf":"https://ijcai.org/proceedings/2019/0551.pdf","markdown":"https://ijcai.org/proceedings/2019/551.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/551","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":551,"pages":{"first":3968,"last":3974},"keywords":["Multidisciplinary Topics and Applications: Biology and Medicine","Machine Learning: Classification","Machine Learning Applications: Bio;Medicine","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Altae-Tran et al., 2017] Han Altae-Tran, Bharath Ramsun- dar, Aneesh S. 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