Learning with Partially Absorbing Random Walks

By Xiao-Ming Wu, Zhenguo Li, Anthony Man-Cho So, John Wright, Shih-Fu Chang. 2012

145 citations recorded by the source corpus.

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Citations in the corpus

Meta-SGD: Learning to Learn Quickly for Few Shot Learning (2017, background): “In semi-supervised learning, one augments target data with massive unlabeled data to leverage a holistic distribution of the data [25].”
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018, methodology): “We choose to use the partially absorbing random walks (ParWalks) (Wu et al. 2012) as our random walk model.”
Propagation kernels: efficient graph kernels from propagated information (2014, methodology): “By utilizing randomwalks (rws)we are able to use efficient off-the-shelf algorithms, such as label diffusion or label propagation (Szummer and Jaakkola 2001; Zhu et al. 2003; Wu et al. 2012), to implement information propagation within the input graphs.”
Designing Fast Absorbing Markov Chains (2014, methodology): “, telecommunication infrastructure and games), and also algorithms, most prominently methods for sampling from large state spaces (Madras 2002), graph-based learning (Page et al. 1999; Fouss et al. 2007; Wu et al. 2012) and local search techniques for combinatorial search and optimization (Aarts and”
Analyzing the Harmonic Structure in Graph-Based Learning (2013, methodology): “Also for the recently proposed partially absorbing random w alks [23], certain setting of absorption rates seems better than others.”
Label Efficient Semi-Supervised Learning via Graph Filtering (2019, methodology): “We compare GLP and IGCN with the stateof-the-art semi-supervised classification methods: manifold regularization (ManiReg) [4], semi-supervised embedding (SemiEmb) [53], DeepWalk [40], iterative classification algorithm (ICA) [43], Planetoid [56], graph attention networks (GAT) [51], multi-layer per”
Label Propagation via Teaching-to-Learn and Learning-to-Teach (2017, background): “Unlike [5]–[7], which worked on asymmetric normalized graph Laplacians, Zhou and Bousquet [8] deployed a symmetric normalized graph Laplacian to implement propagation.”
A survey on semi-supervised learning (2019, methodology): “Wu et al. (2012b) recently applied semi-supervised EM with a naïve Bayes classifier to the problem of detecting fake product reviews on e-commerce websites.”
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks (2019, methodology): “As for the baselines, we opt the Label Propagation (LP) (Wu et al. 2012) using ParWalks; Graph Convolutional Networks (GCNs) (Kipf and Welling 2016); Self-Training, CoTraining, Union and Intersection (Li, Han, and Wu 2018) all based on the confidence of prediction.”
A Graph-Based Push Service Platform (2016, methodology): “user group, and highlighted one based on recent advance in graph mining, the Partially Absorbing Random Walk [13], which leads to substantial”