Predict Anchor Links across Social Networks via an Embedding Approach
Authors: Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin, Xueqi Cheng
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1{CAS Key Lab of Network Data Science and Technology Institute of Computing Technology, Chinese Academy of Sciences, China} 2{University of Chinese Academy of Sciences, China} |
| Pseudocode | No | The paper describes the model algorithmically and provides an illustrative diagram (Figure 1), but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not include any statement about releasing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The first dataset1 was crawled from Facebook and published in [Viswanath et al., 2009]. 1http://socialnetworks.mpi-sws.org/data-wosn2009.html. The second dataset used in this paper is a co-author network... extracted from the Microsoft Academic Graph (MAG) [Sinha et al., 2015]2. 2http://research.microsoft.com/en-us/projects/mag/ |
| Dataset Splits | Yes | PALE (MLP): PALE model with the MLP being employed as the mapping function, where the dimension of the hidden layer is 2 d, the learning rate and the regularizing coefficient are chosen based on a 5-fold cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers that are required to replicate the experiments. |
| Experiment Setup | Yes | To combat this problem, we propose a strategy to identify hidden edges with the help of the observed anchor links and the structure of the other network. ... Finally, we adopt stochastic gradient descent to learn the latent representations. ... In this paper, we consider both linear and non-linear mapping functions. ... For the linear mapping function, is a d d matrix... In addition, we employ Multi-Layer Perceptron (MLP) ... where the dimension of the hidden layer is 2 d, the learning rate and the regularizing coefficient are chosen based on a 5-fold cross-validation. |