Mapping Users across Networks by Manifold Alignment on Hypergraph
Authors: Shulong Tan, Ziyu Guan, Deng Cai, Xuzhen Qin, Jiajun Bu, Chun Chen
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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. |