Representation Learning for Scale-Free Networks
Authors: Rui Feng, Yang Yang, Wenjie Hu, Fei Wu, Yueting Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six datasets show that our algorithms are able to not only reconstruct heavy-tailed distributed degree distribution, but also outperform state-ofthe-art embedding models in various network mining tasks, such as vertex classification and link prediction. |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Zhejiang University, China |
| Pseudocode | No | The paper describes algorithms using text and mathematical equations, but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Facebook (Leskovec and Mcauley 2012): This dataset is a subnetwork of Facebook. Twitter (Leskovec and Mcauley 2012): This dataset is a subnetwork of Twitter. Coauthor (Leskovec, Kleinberg, and Faloutsos 2007): This network covers scientific collaborations between authors. Citation (Tang et al. 2008): Similar to Coauthor, this is also an academic network, where vertexes are authors. |
| Dataset Splits | No | For link prediction, the paper states 'randomly select about 1% pairs of vertexes for training and evaluation', but does not specify a train/validation/test split or provide percentages/counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models) used to run the experiments. |
| Software Dependencies | No | The paper refers to methods like 'word2vec', 'Deep Walk', and 'node2vec' and mentions 'Hierarchical Softmax' for implementation, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Unless otherwise specified, the embedding dimension for our experiments is 200. Specifically, we perform 10 random walks starting from each vertex, and each random walk will have a length of 40. In our proposed models, β R is the model parameter used to indicate the strength of degree penalty. |