Relational Topic Models for Document Networks

By Jonathan D. Chang, David M. Blei. 2009

595 citations recorded by the source corpus.

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Citations in the corpus

Adversarially Regularized Graph Autoencoder (2018, background): “RTM [Chang and Blei, 2009] learns the topic distributions of each document from both text and citation.”
Attributed Graph Clustering: A Deep Attentional Embedding Approach (2019, methodology): “To handle both content and structure information, relational topic models [Sun et al. , 2009; Chang and Blei, 2009], co-clustering method [Guo et al. , 2019], and content propagation [Liu et al. , 2015] have also been widely used.”
Active Multitask Learning Using Both Latent and Supervised Shared Topics (2014, background): “Some other researchers [2, 44, 9] assume that supervision is provided for a single response variable to be predicted for a given document.”
Applications of Topic Models (2017, background): “Conditioning on the topic assignments can improve the algorithm’s ability to predict links on held-out documents, however [Chang and Blei, 2009].”
Mining Text Data (2012, methodology): “Corpus Summarization: Clustering techniques provide a coherent summary of the collection in the form of cluster-digests [83] or word-clusters [17, 18], which can be used in order to provide summary insights into the overall content of the underlying corpus.”
Attributed Graph Clustering via Adaptive Graph Convolution (2019, methodology): “In recent years, various attributed graph clustering meth-ods have been proposed, including methods based on generative models [ Chang and Blei, 2009 ] , spectral clustering [ Xia et al. , 2014 ] , random walks [ Yang et al. , 2015 ] , nonnegative matrix factorization [ Wang et al. , 2016b ] , and…”
A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (2018, background): “We highlight three extensions related to ours: the correlated topic model (Blei and Lafferty 2007), the hierarchical topic model (Blei et al. 2003), and the relational topic model (Chang and Blei 2009).”
A Survey on Context Learning (2017, background): “[43] proposes the relational topic model for documents with links between them, Ref.”
Learning to Discover Social Circles in Ego Networks (2012, methodology): “First we experimented with Mixed Membership Stochastic Block Models [2], which consider only network information, and variants that also consider text attributes [5, 6, 13].”
Community Detection in Networks with Node Attributes (2013, methodology): “Identifying network communities al lows us to discover functionally related objects [15], [16], [41 ], study interactions between modules [2], infer missing attr ibu e values [4], [10], and predict unobserved connections [9].”
A Survey on Network Embedding (2017, background): “Also, they learn the representation of nodes in the topic space based on the Relational Topic Model (RTM) (Chang and Blei 2009), where each topic z is associated with a probability distribution over words.”
Attributed Social Network Embedding (2017, methodology): “To address this shortcoming, [27], [28] were designed to impose additional constraints on the generative model that”
Adaptive Graph Encoder for Attributed Graph Embedding (2020, background): “[2, 5] model features as latent variables in Bayesian networks.”
KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts (2015, methodology): “KB-LDA is an extension to LDA and link-LDA (Blei et al., 2003; Erosheva et al., 2004), modeling documents as a mixed membership over entity types with additional annotated metadata, such as links (Nallapati et al., 2008; Chang and Blei, 2009).”
A Survey of Statistical Network Models (2009, background): “[208] and Chang and Blei [61] suggest different ways to approach this kind of combination model.”