Discrete Embedding for Latent Networks
Authors: Hong Yang, Ling Chen, Minglong Lei, Lingfeng Niu, Chuan Zhou, Peng Zhang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods. We conduct experiments on real-world network data to validate the performance of the DELN model. |
| Researcher Affiliation | Collaboration | 1 Centre for Artificial Intelligence, University of Technology Sydney, Australia 2 Faculty of Information Technology, Beijing University of Technology, China 3 School of Economics and Management, University of Chinese Academy of Sciences, China 4 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China 5 Ant Financial Services Group, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1 Discrete Embedding Latent Networks (DELN) Require: Cascades C, feature X, dimension d, # of iterations τ1 and τ2, parameters T, α, β, Ensure: Discrete representation matrix B 1: Initialize W, Z, B randomly 2: W-Step: Calculate W using Eq.(6) 3: Calculate P using Eq.(1) 4: Repeat until converge or reach τ1 5: Z-Step: Calculate Z using Eq.(8) 6: B-Step: Repeat until converge or reach τ2 7: for l = 1, , d do 8: update bl using Eq.(13) 9: end for 10: return matrix B |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Table 1 summarizes the datasets, where Wiki [Yang et al., 2015] is a network of webpages, Citeseer [Lu and Getoor, 2003; Sen et al., 2008] is a scientific network where the nodes represent papers and the edges are paper citations, Cora [Lu and Getoor, 2003; Sen et al., 2008] is another citation network which focuses on publications in the machine learning area, and Blog Catalog [Huang et al., 2017] is a social network which concerns blog users. |
| Dataset Splits | No | Following the setups in Deepwalk, we randomly sample a portion of nodes for training and the rest for testing. The ratio of training samples ranges from 10% to 90%. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, used for the experiments. |
| Experiment Setup | Yes | For all of the models, we set the embedding dimension as d = 128. The parameters of all baselines are set as the default values. We test DELN with respect to different parameters to validate its robustness. We test the Micro-F1 and Macro-F1 scores of DELN with d varying from 16 to 256. |