Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Exploring social annotations for information retrieval
Proceedings of the 17th international conference on World Wide Web
Joint latent topic models for text and citations
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Effective latent space graph-based re-ranking model with global consistency
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Probabilistic dyadic data analysis with local and global consistency
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A generalized Co-HITS algorithm and its application to bipartite graphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
iTopicModel: Information Network-Integrated Topic Modeling
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Discriminative topic modeling based on manifold learning
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
Collective topic modeling for heterogeneous networks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Modeling and exploiting heterogeneous bibliographic networks for expertise ranking
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
The contextual focused topic model
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Practical collapsed variational bayes inference for hierarchical dirichlet process
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis and refinement of cross-lingual entity linking
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Mining heterogeneous information networks: a structural analysis approach
ACM SIGKDD Explorations Newsletter
AMETHYST: a system for mining and exploring topical hierarchies of heterogeneous data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Link prediction in human mobility networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Tag-weighted topic model for mining semi-structured documents
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
User behavior learning and transfer in composite social networks
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
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With the development of Web applications, textual documents are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about text-rich heterogeneous information networks. Topic models have been proposed and shown to be useful for document analysis, and the interactions among multi-typed objects play a key role at disclosing the rich semantics of the network. However, most of topic models only consider the textual information while ignore the network structures or can merely integrate with homogeneous networks. None of them can handle heterogeneous information network well. In this paper, we propose a novel topic model with biased propagation (TMBP) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way. The underlying intuition is that multi-typed objects should be treated differently along with their inherent textual information and the rich semantics of the heterogeneous information network. A simple and unbiased topic propagation across such a heterogeneous network does not make much sense. Consequently, we investigate and develop two biased propagation frameworks, the biased random walk framework and the biased regularization framework, for the TMBP algorithm from different perspectives, which can discover latent topics and identify clusters of multi-typed objects simultaneously. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on several datasets. Experimental results demonstrate that the improvement in our proposed approach is consistent and promising.