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..
Mapping Users across Networks by Manifold Alignment on Hypergraph
Authors: Shulong Tan, Ziyu Guan, Deng Cai, Xuzhen Qin, Jiajun Bu, Chun Chen
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results have demonstrated the effectiveness of our proposed algorithm in mapping users across networks. Besides, we conduct additional experiments on simulation datasets to investigate the model reliability and settings. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, University of California, Santa Barbara, CA 93106, USA 2 College of Information and Technology, Northwest University of China, Xi an 710127, China 3 State Key Laboratory of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China 4 Zhejiang Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou 310027,China |
| Pseudocode | No | The paper describes the algorithm steps in prose and mathematical equations but does not include structured pseudocode or an explicitly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | Yes | we use data from DBLP (Deng et al. 2011) to construct the original network and consider coauthor relations as social relations. |
| Dataset Splits | No | The paper mentions varying the proportion of training data (e.g., 'using 30% of user correspondences as training data') but does not specify a distinct validation split or explicit training/validation/test percentages/counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We tune the number of nearest neighbors k to achieve the best performance, in our each experiment. The important parameter of MAH is the dimensionality d of the learned space. ... So we set d = (|V X| + |V Y | l)/10 for all other experiments. Weights for this kind of hyperedges are empirically set to 0.1 since they are not so reliable as true social relations. |