NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
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Abstract (arXiv)
Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities. NeuralKG-ind is the first library of inductive KGRL as an important update of NeuralKG library. It includes standardized processes, rich existing methods, decoupled modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy for researchers and engineers to reproduce, redevelop, and compare inductive KGRL methods. The library, experimental methodologies, and model re-implementing results of NeuralKG-ind are all publicly released at https://github.com/zjukg/NeuralKG/tree/ind .
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 34 of its 45 references that are in the map. First posted on arXiv 28 Apr 2023.
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