Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
DANE: Domain Adaptive Network Embedding
Authors: Yizhou Zhang, Guojie Song, Lun Du, Shuwen Yang, Yilun Jin
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 14,663 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,292 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments reο¬ect that the proposed framework outperforms other well-recognized network embedding baselines in cross-network domain adaptation tasks. |
| Researcher Affiliation | Academia | 1School of Electronic Engineering and Computer Science, Peking University 2Key Laboratory of Machine Perception (Ministry of Education), Peking University EMAIL |
| Pseudocode | No | The paper describes the methodology using prose and mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | Paper Citation Networks1 consist of two different networks A and B, where each node is a paper... 1collected from Aminer database [Tang, 2016] |
| Dataset Splits | No | The paper describes using a source network for training and a target network for testing in a domain adaptation context. However, it does not explicitly provide details for a separate validation split within these networks or for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions general software components like 'L2-regularized logistic regression via SGD algorithm' and 't-SNE package' but does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | To be fair, for all methods we set the embedding dimension to 128 on Paper Citation Networks, and 32 on Co-author Networks. For methods applying negative sampling, we set negative sampling number as 5. For methods employing GCN, we use same activation function and 2-layer architecture. |