Noise-Resilient Similarity Preserving Network Embedding for Social Networks
Authors: Zhenyu Qiu, Wenbin Hu, Jia Wu, ZhongZheng Tang, Xiaohua Jia
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments to validate the effectiveness of NSP on several real-world applications: node classification, node clustering, and link prediction. We evaluated the method on four social networks. ... As illustrated in Fig. 2, in these four networks, the classification accuracy of all algorithms decreases with the increase of noise ratio R, especially in the Cora and Polblogs networks. |
| Researcher Affiliation | Academia | Zhenyu Qiu1,2 , Wenbin Hu1,4 , Jia Wu3 , Zhong Zheng Tang2 and Xiaohua Jia2 1School of Computer Science, Wuhan University 2Department of Computer Science, City University of Hong Kong 3Department of Computing, Macquarie University 4Shenzhen Research Institute, Wuhan University, China |
| Pseudocode | Yes | Algorithm 1 Construct Correction Matrix |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code or provide a link to a repository for the methodology described. |
| Open Datasets | Yes | We evaluated the method on four social networks. 1) Blog Catalog is a network of social relationships of bloggers listed on the Blog Catalog website (10312 nodes, 333983 links, and 39 different labels). 2) Cora is a citation network of scientific publications (2708 nodes, 5278 links, and 7 different labels). 3) Email Eu is an email network of a large European research institution (1005 nodes, 25571 links, and 42 different labels). 4) Polblogs is a blog network about US politics recorded in 2005 (1490 nodes, 16715 links, and 2 different labels). |
| Dataset Splits | Yes | The parameters of the comparison algorithms are set to their default values, and the parameters of NSP are tuned by using grid search on the validation set. ... For each class of a given network, we randomly selected 80% of the nodes as the training nodes and the rest as the testing nodes. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions general experimental setup without hardware information. |
| Software Dependencies | No | The paper mentions algorithms like KNN and K-means but does not provide specific version numbers for any software components or libraries, which are necessary for reproducibility. |
| Experiment Setup | Yes | We uniformly set the representation dimension d = 128. The Sv of NSP is consists of two local similarity indexes CN and AA, and two global similarity indexes KI and Sim Rank [Lu and Zhou, 2010]. The parameters of the comparison algorithms are set to their default values, and the parameters of NSP are tuned by using grid search on the validation set. |