What is the difference between graph and vector embeddings?

What is the difference between graph and vector embeddings?

Graph embeddings and vector embeddings are both techniques to represent data in numerical form, but they address different types of data structures and use cases. Vector embeddings convert individual data points (like words, images, or user profiles) into dense vectors of numbers, capturing their semantic or contextual meaning. Graph embeddings, on the other hand, focus on representing nodes, edges, or entire subgraphs within a graph structure (like social networks or recommendation systems) as vectors, preserving their relational and structural properties. The key distinction lies in what they aim to model: vector embeddings prioritize similarity or context, while graph embeddings prioritize connectivity and topology.

Vector embeddings are widely used for unstructured or sequential data. For example, in natural language processing (NLP), tools like Word2Vec or BERT transform words or sentences into vectors so that semantically similar terms (e.g., “cat” and “dog”) are closer in the vector space. Similarly, image embeddings generated by convolutional neural networks (CNNs) encode visual features into vectors for tasks like classification. These embeddings ignore relationships between entities unless explicitly modeled. They work well when the data lacks inherent connections or when the goal is to compare individual items (e.g., finding similar products in an e-commerce catalog).

Graph embeddings, however, are designed for structured relational data. Techniques like Node2Vec, DeepWalk, or Graph Neural Networks (GNNs) generate embeddings by analyzing how nodes interact within a graph. For instance, in a social network, a user’s embedding would reflect not just their profile attributes but also their connections, communities, and roles in the network. This allows tasks like predicting missing links between users or identifying influential nodes. Graph embeddings often incorporate both node features and graph topology, making them suitable for scenarios where relationships are critical—like fraud detection (modeling transaction networks) or recommendation systems (capturing user-item interactions). While vector embeddings are a subset of representation learning, graph embeddings extend this concept to handle interconnected data.

In summary, vector embeddings simplify individual data points into numerical form, while graph embeddings encode the complex relationships within networked data. The choice depends on the data structure: vector embeddings for standalone items, graph embeddings for interconnected systems.