Follow Graph

A follow graph is a directed graph (or digraph) that models asymmetric, one-way connections within a social or information network. In this structure, nodes represent entities like users, wallets, or accounts, while directed edges represent follow relationships. For example, if User A follows User B, an edge points from A to B, but not necessarily from B to A. This is distinct from a symmetric friendship graph, where connections are mutual. Follow graphs are foundational to platforms like Twitter, Mastodon, and Lens Protocol, where they dictate content visibility and feed algorithms.

In blockchain and web3 contexts, the follow graph is a critical primitive for decentralized social networks (DeSo) and on-chain reputation systems. Here, the graph's edges—the follow actions—are often recorded as immutable transactions or events on a blockchain or decentralized data protocol. This creates a transparent, user-owned social graph that is portable across applications. Key implementations include using smart contracts to manage follow NFTs, storing graph data on decentralized storage like IPFS or Ceramic, and leveraging graph-indexing protocols like The Graph for efficient querying of these relationships.

The structure of a follow graph enables several core functionalities: content curation (determining whose posts a user sees), influence measurement (calculating metrics like follower count or PageRank), and community discovery. Analysts can traverse the graph to identify key influencers, map information flow, and detect clusters or communities. In token-curated registries or DAOs, a user's position in the follow graph can inform governance weight or reputation scores, creating sybil-resistant mechanisms based on organic, attested social connections rather than just token holdings.

A follow graph is a directed graph data structure that models asymmetric relationships where one user (a follower) subscribes to the updates of another user (a followee) without requiring mutual consent. This creates a one-way, unidirectional connection, contrasting with the mutual, bidirectional links of a friend graph. In blockchain and Web3 contexts, such as Farcaster or Lens Protocol, these graphs are often stored on-chain or in decentralized networks, making the social connections portable, user-owned, and composable across different applications.

The mechanics are defined by a simple edge list where each edge points from a follower to a followee, represented as (follower_address, followee_address). This structure enables efficient querying for key social metrics: a user's out-degree (who they follow) and in-degree (their followers). Platforms use this to construct personalized feeds by traversing the graph—aggregating content from a user's direct followees or employing algorithms that also include content from second-degree connections (followers-of-followed).

Implementing a follow graph on a blockchain, like Ethereum, typically involves a smart contract with functions to follow and unfollow, which emit events and update the persistent graph state. This on-chain storage is crucial for interoperability and user sovereignty, as it prevents platform lock-in. However, storing large graph data on-chain can be expensive, leading to hybrid designs where only the critical relationship attestations are on-chain, while heavier content is stored off-chain in systems like IPFS or Arweave, referenced via content identifiers (CIDs).

From a network analysis perspective, follow graphs reveal influencer patterns through high in-degree nodes and can identify communities via clustering algorithms. In decentralized social (DeSo) protocols, this graph is a fundamental primitive that developers can compose into new applications—imagine a DAO tool that automatically grants voting power based on a user's follower count or a curation app that surfaces topics trending within one's specific graph neighborhood, all without asking users to rebuild their social network from scratch.

A follow graph is a directed graph data structure that explicitly models one-way, asymmetric relationships, such as "user A follows user B." In web3 and decentralized social (DeSo) protocols like Lens Protocol and Farcaster, this structure underpins the social layer, enabling features like feeds, notifications, and content discovery without relying on a central database. Unlike a friendship graph which implies mutual connection, a follow graph captures the intent and attention flow of users, making it fundamental for modeling influence, reputation, and algorithmic curation in open networks.

Technically, a follow graph is often implemented using an adjacency list or stored as a set of edges in a decentralized database or smart contract. Each edge is a tuple (follower, followee) that can be queried to reconstruct a user's social circle. This structure enables efficient traversal for generating personalized timelines—collecting posts from all followees—and calculating metrics like follower count or network centrality. The integrity of these relationships is secured on-chain or via cryptographic signatures, ensuring users own their social graph and can potentially port it between compatible applications.

The follow graph is a core primitive for social finance (SocialFi) and community governance. Projects leverage it to weight voting power, distribute rewards, or create token-gated access based on social capital. For instance, a user's influence score might be derived from their position in the graph using algorithms like PageRank. This moves beyond simple follower counts to measure the quality and structure of connections, enabling more sophisticated decentralized applications for content moderation, curation markets, and trust-based discovery.

A follow graph is a directed graph data structure that maps the asymmetric relationship of "following" between entities, typically users, within a social network. In Web2, this graph is a proprietary asset owned and controlled by centralized platforms like X (formerly Twitter) or Instagram, which monetize user attention and connections through advertising. The platform's algorithms determine what content is surfaced, creating a walled garden where the social capital and network effects accrued by users are not portable or ownable by the users themselves.

The Web3 paradigm introduces the concept of a decentralized social graph, where follow and connection data is stored on a public blockchain or a decentralized protocol. This shift enables data portability and user sovereignty, allowing individuals to own their social identity and relationships. Protocols like Lens Protocol and Farcaster exemplify this by creating an open social layer where a user's profile, followers, and content are non-custodial assets represented as NFTs or stored in smart contracts, freeing them from platform lock-in.

This architectural change unlocks new models for social finance (SocialFi) and community governance. Developers can build interoperable applications on top of a shared social graph, where a user's reputation and connections persist across different dApps. Furthermore, it enables novel mechanisms like token-gated follows or community-curated feeds, transforming social capital into a verifiable, composable asset within a broader decentralized ecosystem, fundamentally redistributing power and economic value from corporations to users.