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The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible.
Features
- Implementation of the node2vec algorithm for graph embeddings
- Biased random walk strategy to balance breadth-first and depth-first exploration
- Generates node embeddings for classification, clustering, and link prediction
- Reference implementation for the KDD 2016 paper on node2vec
- Scalable to large graphs through efficient sampling and optimization
- Provides reproducible experiments for graph-based machine learning tasks