Aligning Users across Social Networks Using Network Embedding
Authors: Li Liu, William K. Cheung, Xin Li, Lejian Liao
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods. |
| Researcher Affiliation | Academia | 1 BJ ER Center of HVLIP&CC, School of Comp. Sci., Beijing Institute of Technology, Beijing, China 2 Department of Computer Science, Hong Kong Baptist University, Hong Kong, China |
| Pseudocode | Yes | Algorithm 1: Learning Aligned IONE Across Networks |
| Open Source Code | No | The paper does not provide any statement or link indicating the public release of source code for the methodology described. |
| Open Datasets | Yes | For performance evaluation, we employ two real-world social network datasets collected from Foursquare and Twitter [Zhang and Yu, 2015a]. |
| Dataset Splits | No | The paper mentions "training-to-test ratios" and "training sets" but does not provide specific percentages or counts for training, validation, or test splits needed to reproduce the data partitioning of the main model. It only implicitly refers to varying training ratios in Fig 2(b) without specifying values in the text itself for the IONE. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms used (e.g., "stochastic gradient descent", "negative sampling", "SVM-Platt scaling classification model") but does not specify any software libraries or their version numbers. |
| Experiment Setup | Yes | Algorithm 1: Learning Aligned IONE Across Networks... Require: Two networks GX and GY , a set of anchor links Ea, learning rate , # of negative samples K |