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..
Exploiting Mutual Information for Substructure-aware Graph Representation Learning
Authors: Pengyang Wang, Yanjie Fu, Yuanchun Zhou, Kunpeng Liu, Xiaolin Li, Kien Hua
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present extensive experimental results to demonstrate the improved performances of our method with real-world data. |
| Researcher Affiliation | Academia | Pengyang Wang1 , Yanjie Fu1 , Yuanchun Zhou2 , Kunpeng Liu1 , Xiaolin Li3 and Kien Hua1 1University of Central Florida 2Computer Network Information Center, Chinese Academy of Sciences 3Nanjing University |
| 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 evaluate the performance over two real-world check-in datasets [Yang et al., 2014] of New York and Tokyo. |
| Dataset Splits | Yes | We conduct 10-fold cross validation and report the average Accuracy@N. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions various models and networks (e.g., GCN, GAE, DGI) but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In the experiment, we set the number of GCN layer = 2, the input feature size=100, the output feature size = 40, learning rate = 0.001. and L = λr Lr + λj Lj + λs Ls |