The Tractability of the Shapley Value over Bounded Treewidth Matching Games

Authors: Gianluigi Greco, Francesco Lupia, Francesco Scarcello

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental The proposed polynomial-time algorithm has been implemented and tested on classes of graphs having different sizes and treewidth up to three. As a further (more practical) contribution, the algorithm has been implemented and its scalability has been experimentally validated.
Researcher Affiliation Academia Gianluigi Greco, Francesco Lupia, and Francesco Scarcello University of Calabria, Italy ggreco@mat.unical.it, {lupia,scarcello}@dimes.unical.it
Pseudocode No The paper describes the algorithm conceptually and in terms of MSO formulas, but it does not include a formal pseudocode block or algorithm environment.
Open Source Code No The paper mentions implementing a 'JAVA system prototype' and delegating to 'MSO solver Sequoia [Langer, 2013]', but does not state that their own implementation's source code is publicly available or provide a link to it.
Open Datasets Yes In order to assess the performances of the prototype implementation, we considered a benchmark of synthetic instances taken from three distinct families of graphs, namely (complete binary) trees, ladder graphs, and Halin graphs. [...] In addition, we considered some real graphs corresponding to author relationships of scientific works in a university (see [Greco and Scarcello, 2013; 2014]).
Dataset Splits No The paper mentions types of graphs and their sizes but does not specify any training, validation, or test dataset splits.
Hardware Specification Yes Experiments have been performed on a dedicated machine equipped with an Intel Core i7-3770k 3.5 GHz processor, 12 GB (DDR 1600 MHz) of RAM, and operating system Linux Debian Jessie.
Software Dependencies Yes Our Java algorithms were executed on the JDK 1.8.0 05-b13 and the GNU g++-4.9 compiler was used to compile Sequoia.
Experiment Setup No The paper describes the approach and implementation details but does not provide specific experimental setup details like hyperparameters, model training configurations, or system-level settings beyond the general hardware/software environment.