Topic-link LDA: joint models of topic and author community

By Yan Liu, Alexandru Niculescu-Mizil, Wojciech Gryc. International Conference on Machine Learning, 2009

344 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/c36dc28619b5d4461a4674e9b0ffde6ad0b6684e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jure Leskovec
2. Julian McAuley
3. Jian Tang
4. Jaewon Yang
5. William W. Cohen
6. William L. Hamilton
7. Philip S. Yu
8. Stephan Günnemann

In Information Systems

1. Julian McAuley
2. Jure Leskovec
3. Tuan M. V. Le
4. Lu Qin
5. Jian Tang
6. Bart Dhoedt
7. Qiaozhu Mei
8. Reynold Cheng

Citations in the corpus

• Effective Community Search for Large Attributed Graphs (2016, methodology): “In [21, 20], the Link-PLSA-LDA and Topic-Link LDAmodels jointly model vertices’ content and links based on the LDAmodel.”
• Community Detection in Networks with Node Attributes (2013, methodology): “Finally, all existing methods are only able to handle relatively small networks: the networks typically analyzed consist only of thousands of nodes [9], [27], [28], [38].”
• Inferring Networks of Substitutable and Complementary Products (2015, methodology): “LDA associates each document in a corpus d ∈ T with a K -dimensional topic distribution θ d (a stochastic vector, i.e. , k θ d,k = 1 ), which encodes the fraction of words in d that discuss each of the K topics.”
• Clustering attributed graphs: Models, measures and methods (2015, methodology): “LDA is also used by Liu et al. (2009) and Balasubramanyan & Cohen (2011) but as a central approach and in an extended manner to identify latent groups.”
• Block-LDA: Jointly Modeling Entity-Annotated Text and Entity-Entity Links (2014, methodology): “The Topic-Link LDA model (Liu et al., 2009) deals with another related problem of modeling”
• 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].”
• Semantics-driven Event Clustering in Twitter Feeds (2015, methodology): “developed their own version of LDA as well, called TopicLinkLDA [10].”
• Probabilistic Latent Document Network Embedding (2014, methodology): “Finally, we compare the effectiveness of PLANE as a topic model for document network (V-D).”
• A survey of community search over big graphs (2019, methodology): “ethod CODICIL, which augments the original graph by creating new edges based on content similarity, and then performs clustering on the new graph. Another common approach is based on topic models. In [147,135], the Link-PLSA-LDA and Topic-Link LDA models jointly model vertices’ content and links bas”
• Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis (2014, background): “They have played an important role in a variety of data mining tasks, both within the scope of computer science (Blei et al., 2003; Griffiths & Steyvers, 2004; Ramage et al., 2010; Liu et al., 2009; Wang & Blei, 2011) and reaching out to other fields, such”