node2vec: Scalable Feature Learning for Networks

• Motivation
• Code
• Datasets
• Contributors
• References

Motivation

Learning useful representations from highly structured objects such as graphs is useful for a variety of machine learning applications. Besides reducing the engineering effort, these representations can lead to greater predictive power.

The node2vec framework learns low-dimensional representations for nodes in a graph by optimizing a neighborhood preserving objective. The objective is flexible, and the algorithm accomodates for various definitions of network neighborhoods by simulating biased random walks. Specifically, it provides a way of balancing the exploration-exploitation tradeoff that in turn leads to representations obeying a spectrum of equivalences from homophily to structural equivalence.

After transitioning to node v from t, the return hyperparameter, p and the inout hyperparameter, q control the probability of a walk staying inward revisiting nodes (t), staying close to the preceeding nodes (x₁), or moving outward farther away (x₂, x₃). For example, the graph visualization above depicts the color-coded communities exhibiting homophily discovered by node2vec in the Les Misérables Network.

Code

A reference implementation of node2vec in Python is available on GitHub. A high performance implementation is included in SNAP and available on GitHub as well.

Datasets

Links to datasets used in the paper:

• BlogCatalog
• Protein-Protein Interaction Source Preprocessed
• Wikipedia Source Preprocessed
• Facebook
• arXiv ASTRO-PH

Contributors

The following people contributed to node2vec:
Aditya Grover
Jure Leskovec
Vid Kocijan

References

node2vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.