node2vec: Scalable Feature Learning for Networks

TL;DR

node2vec is a scalable algorithm that learns continuous, low-dimensional feature representations for nodes in networks, capturing diverse connectivity patterns to improve prediction tasks like classification and link prediction.

Contribution

It introduces a flexible neighborhood exploration method using biased random walks, enhancing the expressiveness of network embeddings over prior rigid approaches.

Findings

Outperforms existing methods on multi-label classification

Achieves better link prediction accuracy

Demonstrates effectiveness across diverse real-world networks

Abstract

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.