shenweichen/GraphEmbedding
GraphEmbedding
GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for transforming complex network nodes into low-dimensional vector spaces, enabling the integration of relational graph data into machine learning workflows.
The library functions as a dimensionality reduction toolkit and network topology analysis tool. It uses matrix-factorization techniques to preserve global connectivity and employs random-walk sampling with skip-gram based vector optimization to learn numerical representations of nodes.
The framework covers several domain-specific capabilities, including node classification analysis, link prediction modeling, and network topology visualization. It also supports graph-based feature engineering by converting discrete node identifiers into continuous vectors for predictive modeling.
Features
- Graph Embedding Generators - Provides a complete system for generating low-dimensional graph embeddings from complex network nodes.
- Dimensionality Reduction Techniques - Provides a toolkit for dimensionality reduction to preserve connectivity while simplifying complex network data.
- Graph Neighborhood Sampling - Utilizes random-walk algorithms for graph neighborhood sampling to identify local structural relationships.
- Skip-Gram Model Architectures - Implements skip-gram model architectures to optimize node vectors based on neighborhood prediction probabilities.
- Node Embeddings - Implements a framework for learning node embeddings using random-walk sampling and skip-gram optimization.
- Graph Representation Learning - Implements graph representation learning to transform relational network data into machine learning compatible vectors.
- Matrix Factorization Toolkits - Provides matrix factorization toolkits to decompose adjacency matrices and preserve global network connectivity.
- Graph Embeddings - Converts discrete node identifiers into continuous vectors to serve as input features for predictive models.
- Link Prediction - Uses vector embeddings to predict the existence or likelihood of missing connections between nodes.
- Negative Sampling Techniques - Employs negative sampling techniques to optimize loss calculations by contrasting observed node pairs against noise samples.
- Node Classification - Predicts categories or labels for individual network nodes based on their learned numerical representations.
- Graph Topology Analysis - Analyzes structural properties and network topology using learned graph embeddings for classification and prediction.
- Network Topology Visualizers - Reduces high-dimensional graph embeddings into a visual space to analyze global network structure.
- Vector Space Models - Implements vector space models to represent discrete graph identifiers as numerical coordinates for machine learning.
Star history
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