Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence 2022
DOI: 10.24963/ijcai.2022/482
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Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks

Abstract: Representation learning on the attribute-missing graphs, whose connection information is complete while the attribute information of some nodes is missing, is an important yet challenging task. To impute the missing attributes, existing methods isolate the learning processes of attribute and structure information embeddings, and force both resultant representations to align with a common in-discriminative normal distribution, leading to inaccurate imputation. To tackle these issues, we propose a novel graph-or… Show more

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Cited by 9 publications
(2 citation statements)
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“…Heterogeneous graphs are ubiquitous in the real world with their ability to model heterogeneous relationships among different types of nodes, such as academic graphs (Wang et al 2019), social graphs (Cao et al 2021), biomedical graphs (Bai et al 2021), and food graphs (Tian et al 2022b). Accordingly, many heterogeneous graph neural networks (HGNNs) are proposed (Hu et al 2020;Tian et al 2021;Zhang et al 2019) to capture the complex structure and rich semantics in heterogeneous graphs.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Heterogeneous graphs are ubiquitous in the real world with their ability to model heterogeneous relationships among different types of nodes, such as academic graphs (Wang et al 2019), social graphs (Cao et al 2021), biomedical graphs (Bai et al 2021), and food graphs (Tian et al 2022b). Accordingly, many heterogeneous graph neural networks (HGNNs) are proposed (Hu et al 2020;Tian et al 2021;Zhang et al 2019) to capture the complex structure and rich semantics in heterogeneous graphs.…”
Section: Introductionmentioning
confidence: 99%
“…Accordingly, many heterogeneous graph neural networks (HGNNs) are proposed (Hu et al 2020;Tian et al 2021;Zhang et al 2019) to capture the complex structure and rich semantics in heterogeneous graphs. Generally, HGNNs achieve great success in handling graph heterogeneity and have been applied in various real-world applications such as recommendation systems (Tian et al 2022a) and healthcare systems (Wang et al 2021b). However, most HGNNs adhere to the supervised or semi-supervised learning paradigms (Wang et al 2021a), in which the learning process is guided by labeled data.…”
Section: Introductionmentioning
confidence: 99%