Integrated Anchor and Social Link Predictions across Social Networks
Authors: Jiawei Zhang, Philip S. Yu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on two real-world partially aligned networks demonstrate that CLF can perform very well in predicting social and anchor links concurrently. |
| Researcher Affiliation | Academia | University of Illinois at Chicago, IL, USA Institute for Data Science, Tsinghua University, Beijing, China. |
| Pseudocode | No | The paper describes the proposed methods using text and equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | Datasets used in this paper include Foursquare, a famous location-based online social networks, and Twitter, the hottest microblogging social network. For more information about the datasets and the crawling method, please refer to [Zhang et al., 2014b]. |
| Dataset Splits | Yes | These links are partitioned into 3 parts with 5 folds cross validation: 3 folds as the training set, 1 fold as the validation set and the remaining 1 fold as the test set. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions that 'SVM of linear kernel' is used as a base classifier but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | In the experiment, we set αt and αs as 0.6 and c is set as 0.1, whose sensitivities will be analyzed in the following parts. |