Link-Based Classification

By Qing Lu, Lise Getoor. 2003

917 citations recorded by the source corpus.

Subfields: Molecular Biology, Graph Learning

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Citations in the corpus

Semi-Supervised Classification with Graph Convolutional Networks (2016, methodology): “We further compare against the iterative classiﬁcation algorithm (ICA) proposed in Lu & Getoor (2003) in conjunction with two logistic regression classiﬁers, one for local node features alone and one for relational classiﬁcation using local features and an aggregation operator as described in Sen…”
Graph Attention Networks (2017, methodology): “This includes label propagation (LP) (Zhu et al., 2003), semi-supervised embedding (SemiEmb) (Weston et al., 2012), manifold regularization (ManiReg) (Belkin et al., 2006), skip-gram based graph embeddings (Deep-Walk) (Perozzi et al., 2014), the iterative classiﬁcation algorithm (ICA) (Lu & Getoor, ”
Diffusion-Convolutional Neural Networks (2015, methodology): “The CRF-LBP result is quoted from [4], which follows the same experimental protocol.”
Representation Learning on Graphs: Methods and Applications (2017, methodology): “Predicting links in these noisy graphs is an important method for automatically expanding biological datasets and for recommending new directions for wet-lab experimentation [40].”
Attention-based Graph Neural Network for Semi-supervised Learning (2018, background): “ICA [30] generalizes LP by allowing more general local updates.”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “Feature-based models [11], [12], [56] generate features for nodes based on their neighborhood and local network statistics and then apply a classifier like Logistic Regression [57] and Naive Bayes [58] to predict the labels.”
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (2017, methodology): “Illustrative Example: Cora In order to illustrate the filtering effect we discuss above, we analyze a small citation network Cora [24].”
Combining instance-based learning and logistic regression for multilabel classification (2009, background): “It is worth mentioning that similar ideas have recently been exploited in relational learning [Getoor and Taskar, 2007] and collective classiﬁcation [Lu and Getoor, 2003; Ghamrawi and McCallum, 2005].”
Who cares about your Facebook friends? Credit scoring for microfinance (2015, methodology): “Based on the empirical results from the study, we focus our attention to the following techniques: (i) the tangens hyperbolicum function for creating the top node weights, where we down-weight the very popular items as providing less information for the target variable, (ii) sum of the shared nodes ”
Graph Regularized Transductive Classification on Heterogeneous Information Networks (2010, methodology): “Generally, classification can be categorized into two groups: (1) transductive classification [10] [11] [22] [21] [19]: to predict labels for the given unlabeled data; and (2) inductive classification [9] [15] [12] [17] [3]: to construct a decision function in the whole data space.”
Adaptively Connected Neural Networks (2019, methodology): “A total of 11 representative state-of-the-art methods, i.e., ManiReg [1], SemiEmb [49], LP [57], DeepWalk [32], ICA [29], Plan-etoid [50], the newly proposed methods Graph-CNN [15],”
Graph Wavelet Neural Network (2019, neutral): “We compare with several traditional semi-supervised learning methods, including label propagation (LP) (Zhu et al., 2003), semi-supervised embedding (SemiEmb) (Weston et al., 2012), manifold regularization (ManiReg) (Belkin et al., 2006), graph embeddings (DeepWalk) (Perozzi et al., 2014), iterative”
Recommender Systems (2016, neutral): “Several base classifiers have been used within this framework, such as logistic regression [379] and a weighted voting classifier [387].”
Collective Classification in Network Data (2008, methodology): “For a more detailed discussion, see, for example,Taskar, Abbeel, & Koller [55], Sen & Getoor [52], and Sen & Getoor [53].”