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..
Adversarial Network Embedding
Authors: Quanyu Dai, Qiang Li, Jian Tang, Dan Wang
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks. |
| Researcher Affiliation | Academia | 1Department of Computing, The Hong Kong Polytechnic University, Hong Kong 2School of Software, FEIT, The University of Technology Sydney, Australia 3HEC Montreal, Canada 4Montreal Institute for Learning Algorithms, Canada |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The source code will be available online. |
| Open Datasets | Yes | Cora and Citeseer are paper citation networks constructed by (Mc Callum et al. 2000). Wiki (Sen et al. 2008) is a network... Cit-DBLP is a paper citation network extracted from DBLP dataset (Tang et al. 2008). |
| Dataset Splits | No | The paper states 'We range the training ratio from 10% to 90% for comprehensive evaluation' but does not specify validation splits or ratios explicitly. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments. |
| Software Dependencies | No | All experiments are carried out with support vector classifier in Liblinear package (Fan et al. 2008). The paper mentions Liblinear but does not provide its version number. |
| Experiment Setup | Yes | For both Deep Walk and node2vec, the window size s, the walk length l and the number of walks η per node are set to 10, 80 and 10, respectively, for fair comparison. ... Specifically, the generator is a single-layer network with leaky Re LU activations (with a leak of 0.2) and batch normalization ... The number of negative samples K is set to 5... We use RMSProp optimizer with learning rate as 0.001. |