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Logistic Regression for Graph Classification

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Shervashidze,  N
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Tsuda,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Shervashidze, N., & Tsuda, K. (2008). Logistic Regression for Graph Classification. Talk presented at NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008). Whistler, BC, Canada.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C643-0
Abstract
In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression for graphs, which is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics.