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..
Network Representation Learning with Rich Text Information
Authors: Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method and various baseline methods by applying them to the task of multi-class classification of vertices. The experimental results show that, our method outperforms other baselines on all three datasets, especially when networks are noisy and training ratio is small. |
| Researcher Affiliation | Collaboration | Cheng Yang1,2, Zhiyuan Liu1,2 , Deli Zhao2, Maosong Sun1, Edward Y. Chang2 1 Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and Technology, Tsinghua University, Beijing 100084, China 2 HTC Beijing Advanced Technology and Research Center, China |
| Pseudocode | No | The paper describes algorithmic steps in text but does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | Yes | The source code of this paper can be obtained from https://github.com/albertyang33/TADW. |
| Open Datasets | Yes | We evaluate TADW with five baseline methods of representation learning using three publicly available datasets 1. 1http://linqs.cs.umd.edu/projects//projects/lbc/index.html. |
| Dataset Splits | Yes | The training ratio varies from 10% to 50% for linear SVM and 1% to 10% for TSVM. For each training ratio, we randomly select documents as training set and the remaining documents as test set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions software like Liblinear and SVM-Light for implementations, but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We set parameters as follows, walks per vertex γ = 80 and window size t = 10 which are the same with those in the original paper. We select k = 80 and λ = 0.2 for Cora and Citeseer datasets; k = 100, 200 and λ = 0.2 for Wiki dataset. |