NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
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 .
Code (1)
Tasks
Graph Representation LearningKnowledge GraphsRepresentation LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNN-b…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+1Pykg2vec: A Python Library for Knowledge Graph Embedding
Python library for knowledge graph embedding and representation learning.
Graph EmbeddingKnowledge Graph EmbeddingRepresentation LearningA Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representat…
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs
This paper presents a new Python library for anomaly detection in unsupervised learning approaches. The input for the library is a univariate time series representing observations of a given phenomenon. Then, it can iden…
Anomaly DetectionTime SeriesTime Series AnalysisPyGDA: A Python Library for Graph Domain Adaptation
Graph domain adaptation has emerged as a promising approach to facilitate knowledge transfer across different domains. Recently, numerous models have been proposed to enhance their generalization capabilities in this fie…
Domain AdaptationGRAPH DOMAIN ADAPTATIONTransfer Learning