Interest Graph

An interest graph is a network model that connects users, content, and concepts based on shared preferences, activities, and engagements. Unlike a traditional social graph, which maps who you know, an interest graph reveals what you care about. It is constructed by analyzing explicit actions—such as follows, likes, and saves—and implicit signals like reading time, search history, and transactional data. This creates a dynamic map of affinities that is more predictive of future behavior than static social connections.

In blockchain and Web3 contexts, interest graphs are built from on-chain and off-chain data. Key data sources include NFT collections held, token governance participation, DeFi protocol interactions, and engagement with decentralized social media platforms. This allows for the creation of a portable, user-centric identity layer that is not owned by a single platform. Protocols like Lens Protocol and Farcaster are pioneering this approach, enabling applications to surface relevant content, communities, and financial opportunities based on a user's verified on-chain interests.

The primary utility of an interest graph lies in personalization and discovery. For users, it powers tailored feeds, curated marketplaces, and relevant community recommendations. For developers and protocols, it enables precise targeting for airdrops, community building, and governance outreach. By shifting the focus from social connections to authenticated interests, these graphs form a foundational layer for the next generation of user-centric applications, fostering ecosystems where relevance and reputation are derived from verified actions and affinities rather than platform-controlled algorithms.

An interest graph is a graph data structure where nodes represent entities (users, topics, content, tokens) and edges represent connections based on shared interests or interactions. Unlike a social graph, which maps who you know, an interest graph maps what you care about. It is constructed by analyzing explicit actions—such as follows, likes, and subscriptions—and implicit signals like transaction history, content consumption, and on-chain activity. This creates a weighted, directed graph where connection strength indicates the affinity level between entities.

The core mechanism involves data ingestion from multiple sources. For Web3, this includes on-chain data (NFT holdings, DeFi interactions, governance votes), off-chain data (social media activity, community engagement), and declared profile information. This raw data is processed through entity resolution to link disparate data points to a single user or topic. Graph algorithms, such as community detection and link prediction, are then applied to surface latent connections and cluster users into cohorts based on shared financial, social, or creative interests, revealing patterns not apparent in raw transactional data.

A practical application is personalized discovery and recommendation. A protocol can traverse a user's node in the interest graph to find adjacent nodes with high edge weights, suggesting relevant new projects, communities, or assets. For example, a user heavily invested in DeFi yield protocols and participating in DAO governance might be recommended new liquid staking derivatives or relevant governance forums. This moves beyond simple collaborative filtering to a model that understands multidimensional user intent and the semantic relationships between different blockchain-based activities.

For developers, building an interest graph requires a graph database (e.g., Neo4j, Amazon Neptune) or a specialized graph compute engine. The key technical challenge is maintaining a real-time or near-real-time graph as user behavior evolves. This involves implementing stream processing pipelines for on-chain events and social feeds, and continuously running graph algorithms to update community structures and recommendation scores. The resulting graph becomes a foundational data layer for building more engaging, sticky, and intelligent applications across social finance (SocialFi), decentralized advertising, and curated marketplaces.

The analytical power of an interest graph extends to network analysis for projects and investors. By examining the subgraph surrounding a specific protocol, analysts can measure community cohesion, identify influential early adopters, and track the migration of user interest between competing projects. This provides a data-driven alternative to vanity metrics like follower counts, offering deeper insight into genuine user engagement and the organic growth of a project's ecosystem based on shared affinities and behaviors.