Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Multidimensional content eXploration
Proceedings of the VLDB Endowment
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Detecting topic evolution in scientific literature: how can citations help?
Proceedings of the 18th ACM conference on Information and knowledge management
iTopicModel: Information Network-Integrated Topic Modeling
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Topic dynamics: an alternative model of bursts in streams of topics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Probabilistic topic models with biased propagation on heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge from interconnected data: a heterogeneous information network analysis approach
Proceedings of the VLDB Endowment
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Topic modeling on information networks is important for data analysis. Although there are many advanced techniques for this task, few methods either consider it into heterogeneous information networks or the readability of discovered topics. In this paper, we study the problem of topic modeling on heterogeneous information networks by putting forward LSA-PTM. LSA-PTM first extracts meaningful frequent phrases from documents captured from heterogeneous information network. Subsequently, latent semantic analysis is conducted on these phrases, which can obtain the inherent topics of the documents. Then we introduce a topic propagation method that propagates the topics obtained by LSA on the heterogeneous information network via the links between different objects, which can optimize the topics and identify clusters of multi-typed objects simultaneously. To make the topics more understandable, a topic description is calculated for each discovered topic. We apply LSA-PTM on real data, and experimental results prove its effectiveness.