Manifold-based Similarity Adaptation for Label Propagation

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Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification (2020, background): “…(GNNs) [ Kipf and Welling, 2016; Hamilton et al. , 2017; Xu et al. , 2018b; Liao et al. , 2019; Xu et al. , 2018a ] and Label Propagation Algorithms (LPAs) [ Zhu et al. , 2003; Zhang and Lee, 2007; Wang and Zhang, 2007; Karasuyama and Mamitsuka, 2013; Gong et al. , 2016; Liu et al. , 2019 ] .”
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (2019, methodology): “ghts in the input graph, these methods are classified as: (1) Edge weights are assumed to be given as input and therefore fixed [1, 37, 38]; (2) Edge weights are parameterized and therefore learnable [10, 21, 35]. Inspired by these methods, we design a module of label smoothness regularization in ou”
Label Propagation via Teaching-to-Learn and Learning-to-Teach (2017, methodology): “Several methods [39], [43] have been proposed to decide the optimal ξ via entropy minimization [39] or local reconstruction [43].”
Unifying Graph Convolutional Neural Networks and Label Propagation (2020, methodology): “…edges in the graph, which is known as Label Propagation Algorithm (LPA) (Zhu et al., 2005; Zhou et al., 2004; Zhang & Lee, 2007; Wang & Zhang, 2008; Karasuyama & Mamitsuka, 2013; Gong et al., 2017; Liu et al., 2019a); On the node side V , node features are propagated along edges and transformed…”