Social Network Analysis

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Social Network Analysis

The Social Network Analysis group works on statistical models for analyzing relational data. One focus lies on machine learning methods for predicting unobserved relations between entities, e.g. which users are supposed to be friends, which products a customer might want to buy in the future, etc. Such problems often deal with relations of higher-order, are multi-relational and the setting is highly sparse. Our research deals among others with matrix and tensor factorization models, sequential models and advanced learning techniques.

Members

Assistant Prof. Dr. Steffen Rendle
Immanuel Bayer
Thierry Silbermann

Teaching

LectureFactorization Machines for Machine Learning
SeminarKDD Data Mining Cup 2013
ProjectSocial Network Analysis

Publications

Software

  • libFM is a library for factorization machines (FM). It can mimic several state-of-the-art factorization models including SVD++, PITF or FPMC just by specifying the input data.
  • Tag Recommender implements the Pairwise interaction tensor factorization PITF for recommending personalized tags.
  • MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms.

News