A collection of tools and algorithms suitable to work with paths (e.g., navigational).
Currently, this tool box consists of a framework for Markov chains. It is possible to fit trail corpora with varying order Markov chain models. The framework includes several advanced methods for determining the appropriate Markov chain order (maximum likelihood methods, information-theoretic methods, Bayesian inference and cross validation prediction) [1].
Furthermore, the framework includes a class for calculating semantic similarity between concepts in human navigational paths [2].
For a basic introduction to the framework please refer to the test examples. For further questions please conduct the issues section or drop me an email!
If you use the code, please cite the corresponding publication.
[1] Philipp Singer, Denis Helic, Behnam Taraghi and Markus Strohmaier, Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order PLoS ONE, vol 9(7), 2014
[2] Philipp Singer, Thomas Niebler, Markus Strohmaier and Andreas Hotho, Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia, International Journal on Semantic Web and Information Systems (IJSWIS), vol 9(4), 41-70, 2013
