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Maps around me: 3D multiview layouts in immersive spaces

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dc.contributor.authorSatriadi, K.A.
dc.contributor.authorEns, B.
dc.contributor.authorCordeil, M.
dc.contributor.authorCzauderna, T.
dc.contributor.authorJenny, B.
dc.date.issued2020
dc.descriptionData source: , https://doi.org/10.1145/3427329 Link to a related website: https://dl.acm.org/doi/pdf/10.1145/3427329, Open Access via Unpaywall
dc.description.abstractMultivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional Network) model in node classification tasks, and can significantly reduce time cost.
dc.identifier.citationProceedings of the ACM on Human-Computer Interaction, 2020; 4(201):1-20
dc.identifier.doi10.1145/3427329
dc.identifier.issn2573-0142
dc.identifier.issn2573-0142
dc.identifier.urihttps://hdl.handle.net/11541.2/25406
dc.language.isoen
dc.publisherACM
dc.rightsCopyright 2020 Association for Computing Machinery
dc.source.urihttps://doi.org/10.1145/3427329
dc.subjectimmersive space
dc.subjectmultiscale exploration
dc.subjectmultiview maps
dc.subjectvirtual reality
dc.titleMaps around me: 3D multiview layouts in immersive spaces
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9916553930601831

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