Graph Invariant Kernels
Authors: Francesco Orsini, Paolo Frasconi, Luc De Raedt
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental evaluationIn Table 1 we show the classification accuracy that we achieved without (k ATTR = 1) and with (k ATTR = RBF(γ)) kernel on continuous attributes. |
| Researcher Affiliation | Academia | 1Department of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200A 3001 Heverlee, Belgium {francesco.orsini,luc.deraedt}@cs.kuleuven.be2Department of Information Engineering Universit a degli Studi di Firenze Via di Santa Marta 3 I-50139 Firenze, Italy paolo.frasconi@unifi.it |
| Pseudocode | No | The paper describes algorithmic steps and presents mathematical equations, but it does not include a distinct section or figure explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper states 'The code of GIKs was written in the Python programming language', but it does not provide an explicit statement about the public availability of this code or a link to a repository. |
| Open Datasets | Yes | PROTEINS and ENZYMESSYMM are sets of proteins from Dobson and Doig (2003) and from the BRENDA database [Schomburg et al., 2004], respectively. ... QC is a dataset for question classification with fixed split (5452 train / 500 test) originally proposed in [Li and Roth, 2002]. |
| Dataset Splits | Yes | Except for QC and WEASEL the accuracy was estimated by 10-times 10-fold cross-validation reporting means and standard deviations [Feragen et al., 2013]. The SVM regularization parameter was selected with an internal k-fold crossvalidation on the training data (k = 3 except k = 10 for QC and WEASEL). |
| Hardware Specification | Yes | The experiments were run on a 16 cores machine (Intel Xeon CPU E5-2665@2.40GHZ and 96GB of RAM). |
| Software Dependencies | No | The paper mentions 'The code of GIKs was written in the Python programming language, while for GRAPHHOPPER we used the MATLAB implementation', but it does not specify version numbers for Python, MATLAB, or any other critical software libraries. |
| Experiment Setup | Yes | Based on preliminary experiments, we fixed R = 3. The Gram matrices were normalized as proposed in [Costa and De Grave, 2010]: for each radius a different Gram matrix is extracted then normalized, we compute their sum and apply normalization again. For ENZYMESSYMM, PROTEINS, SYNTHETICNEW we chose the parameter γ of the RBF kernel as in [Feragen et al., 2013], to be 1/d ATTR which is the inverse of the number of dimensions of the attributes. |