Also named #Graph Mining and Analysis with Python#
Advanced machine learning and combinatorial methods for large scale text and graph data
The course is a part of the Master Data Science in Ecole Polytechnique
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Advanced graph kernels and classification,clustering / community mining (Louvain, modularity, degeneracy),
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Influence maximization models (SIR/SIS, LT, IC,…), degeneracy based spreaders selection.
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Graph of words advanced topics: tw-icw, graph kernels for document similarity, graph based regularization for text classification
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Word embeddings, Unsupervised document classification with the Word Mover’s Distance, WMD vs cosine similarity
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Deep learning for NLP, Supervised document classification (TF-IDF vs TW-IDF), CNNs for sentence classification
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Keyword extraction for summarization: Graph based keyword extraction, summarization (off line, online), Filipova’s word graph for multi-sentence fusion.
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Bayesian Reasoning and Machine Learning, David Barber c 2010
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Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer,ISBN-13: 978-0387310732 Text Web Mining
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Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. (http://nlp.stanford.edu/IR-book/)
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Web Data Mining, Exploring Hyperlinks, Contents, and Usage Data, Bing Liu, Second Edition, July 2011, http://www.cs.uic.edu/~liub/WebMiningBook.html
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Practical Graph Mining With R, Nagiza F. Samatova, et.al., Chapman & Hall/CRC Data Mining and Knowledge Discovery Series (2013) http://www.csc.ncsu.edu/faculty/samatova/practical-graph-mining-with-R/PracticalGraphMiningWithR.html Bigdata
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Frontiers in Massive Data Analysis, http://www.nap.edu/catalog.php?record_id=1837