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Found 6 papers, 5 papers with code
Interpretable Network Representation Learning with Principal Component Analysis
We consider the problem of interpretable network representation learning for samples of network-valued data.
Nonparametric Feature Impact and Importance
In this paper, we give mathematical definitions of feature impact and importance, derived from partial dependence curves, that operate directly on the data.
Technical Report: Partial Dependence through Stratification
Partial dependence curves (FPD) introduced by Friedman, are an important model interpretation tool, but are often not accessible to business analysts and scientists who typically lack the skills to choose, tune, and assess machine learning models.
Fast embedding of multilayer networks: An algorithm and application to group fMRI
Learning interpretable features from complex multilayer networks is a challenging and important problem.
Social and Information Networks Physics and Society
Topic supervised non-negative matrix factorization
Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents.
A testing based extraction algorithm for identifying significant communities in networks
In addition, we carry out a simulation study to assess the effectiveness of ESSC in networks with various types of community structure, including networks with overlapping communities and those with background vertices.
