Topic modeling with network regularization

By Qiaozhu Mei, Deng Cai, Duo Zhang, Chengxiang Zhai. The Web Conference, 2008

415 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c3c7bb20082b334cb67c857819d360afe1799c6e

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Citations in the corpus

A Survey of Text Clustering Algorithms (2012, methodology): “A natural approach to network-based topic modeling is to add a network-based regularization constraint to traditional topic models such as NetPLSA [65].”
Recommender systems with social regularization (2011, methodology): “In [26], Mei et al. proposed a method combines topic modeling and social network analysis, and leveraged the power of both statistical topic models and discrete regularization.”
Mining Text Data (2012, methodology): “[46] in detail since it is representative of combining PLSI and network analysis.”
A Survey on Context Learning (2017, background): “In [1], they extract all the papers from four computer science conferences, WWW, SIGIR, KDD and NIPS as the testing dataset and set the number of topics to be 4 accordingly.”
Geographical topic discovery and comparison (2011, methodology): “We use NetPLSA in the text-driven model.”
Label Informed Attributed Network Embedding (2017, background): “Mei et al. [25] designed a harmonic regularization based embedding framework to tackle the problem of topic modeling with network structure.”
Relational Topic Models for Document Networks (2009, background): “(2008) and Mei et al. (2008), which attempts to address these issues by extending the mixed-membership stochastic block model (Airoldi et al.”
Interactive Machine Learning via a GPU-accelerated Toolkit (2017, background): “Since the latent topics extracted by the algorithm are not always semantically meaningful [29, 7], different constrained topic models [27, 28] have been developed.”
Topic-link LDA: joint models of topic and author community (2009, background): “We thank John La.erty, Jure Leskovec, Ramesh Nallapati for discussing the ideas in the paper.”
Cross-Lingual Latent Topic Extraction (2010, methodology): “This optimization problem can be solved using a Generalized Expectation-Maximization (GEM) algorithm as described in (Mei et al., 2008a).”
Probabilistic Latent Document Network Embedding (2014, background): “One example is through a regularization framework [22], which however is not a joint model, and therefore does not model the generation of links.”
Attributed Social Network Embedding (2017, methodology): “models such as NetPLSA [32] and RTM [27].”
Social influence analysis in large-scale networks (2009, methodology): “In addition, we can use statistical topic modeling [3, 15, 18] to automatically extract topics from the social networking data.”
A multi-view probabilistic model for 3D object classes (2009, methodology): “Similar to [20], we update τ in a greedy approach.”