Table2Graph: Transforming Tabular Data to Unified Weighted Graph
Authors: Kaixiong Zhou, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, Xia Hu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |