Node2Vec

The Node2Vec model is a method for learning low-dimensional vector representations of nodes in a graph. It is based on the concept of language embeddings, where words with similar contexts are represented by similar vectors. Similarly, in the context of graph data, nodes with similar structural roles are expected to have similar embeddings.

Node2Vec uses a random walk strategy to generate sequences of nodes based on their neighborhood relationships. By sampling different types of walks, the model captures both local and global graph structure. These random walks are then used to train a Skip-gram model, which learns vector representations of nodes that preserve the structural information from the graph.

1. Recommender Systems: Node2Vec can be used to generate embeddings for users and items in a recommendation system, capturing their similarities based on their relationships in a graph (e.g., user-item interactions, item co-occurrence).
2. Link Prediction: By encoding the graph structure into embeddings, Node2Vec can help predict missing or future links in a network, aiding in tasks like social network analysis, knowledge graph completion, or identifying potential collaborations.
3. Community Detection: The learned node embeddings can be utilized to cluster nodes based on their structural roles and identify communities within a graph.

• Node2Vec: Scalable Feature Learning for Networks (Paper): The original research paper introducing Node2Vec.
• Node2Vec Python Library (GitHub): Implementation of Node2Vec in Python.
• Graph Embedding with Node2Vec (Tutorial): A tutorial providing step-by-step implementation guidance for Node2Vec.

1. Aditya Grover: The primary author of the Node2Vec paper and the creator of the Node2Vec Python library.
2. Bryan Perozzi: Contributed to the development of Node2Vec research and has expertise in graph embedding techniques.
3. Jure Leskovec: An expert in network science and graph mining, including the development of scalable and interpretable embedding methods.
4. Ryan A. Rossi: Researcher with expertise in graph analytics, network representation learning, and data mining.
5. William L. Hamilton: Expert in graph representation learning, with research contributions on methods including Node2Vec and GraphSAGE.