Scalable Text and Link Analysis with Mixed-Topic Link Models
| Autor: | Lise Getoor, Cristopher Moore, Xiaoran Yan, Yaojia Zhu |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Topic model
I.2 FOS: Computer and information sciences H.4 Computer science G.3 Inference FOS: Physical sciences Machine Learning (stat.ML) computer.software_genre Computer Science - Information Retrieval Machine Learning (cs.LG) H.3.3 Stochastic block model Statistics - Machine Learning Node (computer science) Link analysis Social and Information Networks (cs.SI) Information retrieval Document classification Computer Science - Social and Information Networks Hyperlink Computer Science - Learning Physics - Data Analysis Statistics and Probability Pairwise comparison computer Information Retrieval (cs.IR) Data Analysis Statistics and Probability (physics.data-an) |
| Zdroj: | KDD |
| DOI: | 10.48550/arxiv.1303.7264 |
| Popis: | Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node and the links between them. In this paper, we combine classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes. Comment: 11 pages, 4 figures |
| Databáze: | OpenAIRE |
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