2019/01/22 by Alexandru Mara, Jefrey Lijffijt, Tijl De Bie
Computer Science · Physics and Astronomy · #Abstraction #Advanced Graph Neural Networks #Artificial intelligence #Complex Network Analysis Techniques #Computation #Computer science #Data mining #Embedding #Enhanced Data Rates for GSM Evolution #Heuristics #Programming language #Python (programming language) #Theoretical computer science #Toolbox #cs.SI
paper · pdf · doi:10.1016/j.softx.2022.100997
arxiv created 2019/01/22 · openalex publication_date 2022/01/01 · arxiv updated 2022/02/02 · openalex created_date 2022/02/08 · openalex updated_date 2026/08/01
In this paper we introduce EvalNE, a Python toolbox for network embedding evaluation. The main goal of EvalNE is to aid researchers and practitioners in performing consistent and reproducible evaluations of new embedding methods, replicating existing evaluations, and conducting benchmark studies. The toolbox can evaluate models independently of their programming language and assess the quality of learned representations through data visualization and downstream tasks such as sign and link prediction, network reconstruction, and node multi-label classification. EvalNE streamlines evaluation by providing automation and abstraction for tasks such as hyperparameter tuning and model validation, node and edge sampling, node-pair embedding computation, and performance reporting. As a command line tool, configuration files describe the evaluation setup and guarantee consistency and reproducibility. As an API, EvalNE provides the building blocks to design any evaluation setup while minimizing the risk of evaluation errors.