Node2vec

Visit Tool

node2vec is an open-source Python implementation of the node2vec algorithm for scalable feature learning in networks. It enables embedding nodes and edges for various machine learning tasks.

Claim this tool

2Views

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is node2vec?

node2vec provides a Python3 implementation of the node2vec algorithm, designed for scalable feature learning in networks. It allows users to generate node embeddings from graphs, which can then be used for tasks like node classification, link prediction, and visualization. The tool supports various parameters for customizing the embedding process, including dimensions, walk length, and the number of walks per node. It also offers functionality for embedding edges using methods like Hadamard, Average, WeightedL1, and WeightedL2. The implementation is open-source and integrates with `gensim.Word2Vec` for model fitting and vector operations, making it a powerful tool for researchers and practitioners working with graph data.

Best used for

Ideal for researchers and data scientists who need to extract meaningful features from network data, generate node and edge embeddings, and apply these to machine learning tasks. Especially valuable for academic research and projects involving graph analysis and representation learning.

Common actions

generate node embeddings

learn graph features

embed network data

analyze graph structures

face swapping"AI Agents"github copilotautomated workflowdeepfakeworkflowslow-code/no-codecollaborationopen-source

Capabilities

Key features

Node embedding generation
Edge embedding methods
Customizable walk parameters
Gensim Word2Vec integration
Scalable feature learning

Target Audience

professorresearcherdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary purpose of the node2vec algorithm?

The node2vec algorithm is designed to learn continuous feature representations (embeddings) for nodes in a network. These embeddings capture the structural and relational properties of nodes, making them suitable for various downstream machine learning tasks like classification and clustering.

Does node2vec support weighted graphs?

Yes, the node2vec implementation supports weighted graphs. Users can specify a `weight_key` parameter during initialization to indicate the attribute in the graph that represents edge weights, allowing the algorithm to incorporate this information into the walk generation process.

Are there any known limitations when running node2vec on Windows?

Yes, due to a known issue with the `joblib` library, parallel execution for walk generation and probability precomputation does not work on Windows. Users on Windows must set the `workers` parameter to 1 in the Node2Vec constructor for the algorithm to function correctly.

Trending

Subcategories trending in Research & Education

Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce