Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Table2Graph: Transforming Tabular Data to Unified Weighted Graph
Authors: Kaixiong Zhou, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results in a variety of real-world applications demonstrate the effectiveness and efficiency of our Table2Graph, in terms of the prediction accuracy and feature interaction detection. |
| Researcher Affiliation | Collaboration | Kaixiong Zhou1 , Zirui Liu1 , Rui Chen2 , Li Li2 , Soo-Hyun Choi3 and Xia Hu1 1Department of Computer Science, Rice University 2Samsung Research America 3Samsung Electronics |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | we adopt two benchmark datasets from the financial fraud detection and online advertising: Creditcard [Dal Pozzolo et al., 2015] and Criteo1. ... For the large graph modeling, we adopt Movie Lens dataset commonly evaluated in the previous collaborative filtering work [He et al., 2017]. |
| Dataset Splits | Yes | Following the common evaluation process, we use 10-fold cross validation and judge models with metrics AUC (Area Under the ROC curve) and Log Loss for real-world datasets Creditcard and Criteo. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The hyperparameters of λ1, λ2 and λ3 are determined based on the grid search, and their influences are empirically studied in the following experiments. ... We adopt two popular item-to-item collaborative filtering methods as the underlying recommendation frameworks, namely factored item similarity model (FISM) [Kabbur et al., 2013] and neural attentive item similarity model (NAIS) [He et al., 2018]. ... A three-layer GNN model is used to learn the feature interactions based upon the generated graph. |