How to Design a Social Graph for DAO Member Coordination

A DAO social graph is a data structure that models the network of relationships between participants. Unlike a traditional social network focused on personal connections, a DAO graph tracks on-chain and off-chain interactions relevant to governance and collaboration. Key entities (nodes) include member wallets, proposals, projects, and smart contracts. Edges (connections) represent actions like voting, delegating, co-authoring proposals, or contributing to a shared treasury. This structured data layer is essential for moving beyond simple token-weighted voting to systems that recognize contribution history, expertise, and trust.

Designing an effective graph starts by defining its primary purpose and data sources. Common objectives include: calculating reputation scores, optimizing delegation, forming working groups, or distributing grants. You must then identify which interactions to capture. On-chain sources are verifiable and include voting history (e.g., on Snapshot or Tally), token transfers, and smart contract interactions (like staking in a liquidity pool). Off-chain sources from platforms like Discord, GitHub, or Discourse can provide qualitative data on communication and code contributions, though they require secure attestation methods like Verifiable Credentials or oracle networks.

The technical implementation involves choosing a data model and storage solution. A common approach uses a graph database like Neo4j or Dgraph to natively handle complex relationship queries. For a simplified start, you can model relationships in a structured SQL database. A basic schema might include tables for members, proposals, and a votes join table that records each vote (member_id, proposal_id, vote_weight, direction). For more advanced analysis, you can compute derived metrics like a member's proposal success rate or their co-voting alignment with others, which strengthens the graph's analytical value.

To make the social graph actionable, you need to expose its data through an API and build user-facing features. An API allows other dApps to query member relationships, such as "show me all contributors to this grant project" or "find delegates aligned with my voting history." Front-end applications can then visualize these networks, display reputation dashboards, or suggest optimal working groups. Protocols like Lens Protocol or CyberConnect offer composable social graph primitives, but for DAO-specific coordination, you will likely need to build a custom layer atop these base protocols or your own data store.

Finally, consider privacy and incentives. While transparency is a DAO tenet, members may want control over how their interaction data is used. Implement privacy-preserving techniques like zero-knowledge proofs for private reputation or allow members to opt-in to graph features. To ensure the graph stays active and accurate, align its use with DAO incentives. For example, reward members for maintaining a complete profile, or use graph-based reputation to weight community grants. A well-designed social graph transforms a DAO from a loose collective into a coordination machine, enabling smarter, more human-centric governance.

A social graph is a data model that maps relationships and interactions between entities. In a DAO context, this means representing members, their roles, contributions, and connections. You must decide on the graph schema: what are the nodes (e.g., Member, Proposal, Project) and what are the edges (e.g., VOTED_ON, CONTRIBUTED_TO, COLLABORATED_WITH). This schema defines what your coordination system can measure and incentivize. Tools like The Graph Protocol for indexing or Ceramic Network for mutable data streams are common building blocks.

Your design must be anchored in the DAO's coordination primitives. Identify the key actions you want to track: is it governance participation (votes, delegations), work contributions (PRs, completed tasks), or social capital (endorsements, mentorship)? Each primitive becomes an edge in your graph. For example, an on-chain voting contract emits events that can be indexed to create a VOTED_ON edge between a member node and a proposal node. Start by auditing your DAO's existing tools—like Snapshot, SourceCred, or Coordinape—to understand what data is already generated.

You will need to interact with blockchain data. Familiarity with EVM-based chains (Ethereum, Polygon, Arbitrum) is essential, as most DAO tooling resides there. Understand how to read events from smart contracts using libraries like ethers.js or viem. For the design phase, you should be comfortable with GraphQL for querying indexed data and basic NoSQL database concepts (like document stores) for structuring node and edge data. Setting up a local test environment with Hardhat or Foundry to simulate on-chain actions is highly recommended.

Finally, consider the privacy and composability trade-offs. Will your graph use on-chain data (transparent, verifiable) or off-chain data (richer, private)? A hybrid approach is common. You also need to plan for sybil resistance; will you use proof-of-personhood (like World ID), token-gating, or a stake-based system to validate graph nodes? Defining these prerequisites clearly prevents architectural dead ends and ensures your social graph effectively reduces coordination overhead for DAO members.

A DAO's social graph is a data structure that models the relationships between its members, assets, and actions. Unlike a traditional organizational chart, it is a decentralized, permissionless, and composable network. Core entities typically include identities (wallets, ENS names), contributions (proposals, code commits, forum posts), reputation (non-transferable tokens, voting power), and relationships (collaborations, delegations, follows). This graph enables the DAO to move beyond simple token-weighted voting to nuanced, context-aware coordination, surfacing key contributors and mapping the flow of influence and information.

Design begins with defining the primitives—the fundamental nodes and edges of your graph. For member coordination, essential nodes are Identity, Contribution, and Skill. Edges define relationships like authored, voted-on, collaborated-with, or endorses. These should be stored on-chain for transparency and portability, using standards like EIP-712 for signed messages or Ceramic for mutable data. For example, a Contribution node could link to an on-chain transaction hash for a governance vote or an IPFS hash for a forum post, creating an immutable record of member activity.

The graph must be sybil-resistant to accurately reflect genuine human coordination. This is achieved by layering attestations and verifiable credentials. Protocols like Ethereum Attestation Service (EAS) or Verax allow members to issue on-chain attestations for skills, completed work, or delegated votes. A developer could receive a SolidityProficiency attestation from a recognized auditor, which becomes a trusted node in their identity. Combining on-chain activity (like consistent project contributions) with social attestations creates a robust, attack-resistant representation of member reputation and trust.

To enable coordination, the graph needs a query layer that turns data into insight. This involves indexing on-chain events and off-chain data into a queryable database using tools like The Graph or Goldsky. A subgraph could track all proposal engagements, allowing you to query: "Which members have co-authored successful proposals together?" or "What is the average reputation score of voters supporting a specific initiative?" These insights power interfaces for finding collaborators, forming working groups, or delegating votes based on expertise rather than just token holdings.

Finally, design for composability and interoperability. A DAO member's graph should not be siloed. Using cross-chain attestation protocols or decentralized identifiers (DIDs), reputation and relationship data can be portable across DAOs and platforms. A contributor's proven track record in one DAO could be verifiably presented when joining another. The graph should expose a public API or adhere to emerging standards like OpenGraph, allowing any front-end client—from a custom dashboard to a governance UI—to query and visualize the network, putting the power of collective intelligence directly in the hands of its members.

A social graph is a data structure that maps relationships between entities. For a DAO, this typically means modeling connections between members, such as follows, collaborations, endorsements, or delegations. Storing this graph on-chain via a smart contract creates a transparent, verifiable, and programmable layer of social context. This enables applications like reputation-weighted voting, trust-based task assignment, and discovery of influential contributors. Unlike off-chain social networks, an on-chain graph's connections are public goods, owned by the users who create them and composable by any other application.

The core contract design involves two primary data structures. First, a mapping to store a user's connections, often using the address => address[] pattern for a simple follow graph. For more complex, labeled relationships (e.g., "endorsed", "collaborated-with"), use a nested mapping: mapping(address => mapping(bytes32 => address[])). The bytes32 key acts as a relationship type. Second, you need events to log graph updates, making them easily indexable by front-ends and subgraphs. Essential functions include follow(address follower, address followed) and unfollow, which must update the mappings and emit corresponding events.

Here is a minimal, foundational example of a DAOSocialGraph contract:

solidityCopy
contract DAOSocialGraph {
    // user => list of addresses they follow
    mapping(address => address[]) public following;
    // user => list of addresses that follow them
    mapping(address => address[]) public followers;

    event Followed(address indexed follower, address indexed followed);
    event Unfollowed(address indexed follower, address indexed followed);

    function follow(address _toFollow) external {
        require(_toFollow != msg.sender, "Cannot follow self");
        following[msg.sender].push(_toFollow);
        followers[_toFollow].push(msg.sender);
        emit Followed(msg.sender, _toFollow);
    }

    function unfollow(address _toUnfollow) external {
        // Implementation to remove from arrays omitted for brevity
        emit Unfollowed(msg.sender, _toUnfollow);
    }
}

This contract establishes a directed graph where edges represent "follows". The followers mapping is maintained for efficient reverse lookups.

For production use, consider critical enhancements. Gas optimization is paramount; storing arrays can become expensive. Consider using EnumerableSet from OpenZeppelin for O(1) add/remove/contains checks. To add semantic meaning, implement a struct for edges: struct Edge { address target; bytes32 relationshipType; uint256 weight; }. This allows for weighted, typed relationships like a "skill-endorsement" with a weight of 5. Access control can be added to let DAO admins curate certain relationship types or to allow users to make some connections private (e.g., via encryption). Always emit rich events to facilitate off-chain indexing and analytics.

Integrate the social graph with other DAO modules to unlock coordination. A voting contract can query a user's reputation score derived from their incoming endorsement edges. A funding or grant committee can auto-populate a reviewer list based on expertise graphs. A task board contract can assign work to users followed by the relevant project lead. By composing the graph with other contracts, you move from simple membership lists to a context-aware organizational OS. Reference implementations can be found in projects like Lens Protocol (general social) and Coordinape (DAO-specific coordination).

When deploying, plan for upgradability (using a proxy pattern) as relationship types and logic will evolve. Be mindful of storage costs for users; consider having them sign off-chain messages (EIP-712) for relationship updates that can be batched and submitted by a relayer. Finally, design the graph with composability in mind: use standard event signatures and public view functions so that any front-end or analytic tool can build on top of it. The goal is to create a resilient, user-owned infrastructure layer that makes DAO coordination more efficient and human-centric.

A social graph for a DAO is a data structure that maps the relationships between its members. This goes beyond simple token holdings to include voting history, proposal creation, delegation links, committee membership, and collaboration on funded projects. By indexing this data into a queryable subgraph on The Graph, you create a foundational layer for building advanced coordination tools like reputation systems, expert discovery, and automated task assignment. The core entities typically include Member, Proposal, Vote, and Delegation.

Start your schema design by identifying the key relationships. A Member entity should have fields for their wallet address, token balance, and links to other entities. Crucially, it should include arrays of relationships: delegatedTo and delegatedFrom to map delegation flows, votes to link to their voting record, and proposalsCreated. A Proposal entity links to its creator and has an array of votes. Each Vote entity connects a Member to a Proposal and records their choice and voting power. This creates a web of explicit, on-chain connections.

In your subgraph's subgraph.yaml manifest, you must define the data sources. For a DAO on Ethereum, this usually involves indexing events from the governance token contract (e.g., Transfer for balances), the governor contract (e.g., ProposalCreated, VoteCast), and potentially a delegation contract. Your mapping functions, written in AssemblyScript, will handle these events to create and update the Member, Proposal, and Vote entities in your schema. For example, a handleVoteCast function will load or create the Member and Proposal, then save a new Vote entity linking them.

With the subgraph deployed, you can query for insights impossible from raw chain data alone. A query to find the most influential delegates would traverse the graph: find Member entities where delegatedFrom is not empty, sum the voting power delegated to them, and sort. To form a working group, you could query for members who have voted similarly on a set of proposals or who have co-authored successful grants. These queries, powered by GraphQL, enable applications to build features like "Find an Expert" or "Delegate Your Vote" based on proven, on-chain activity.

For robust coordination, consider extending the graph with off-chain data. Using The Graph's Grafting feature, you can base a new subgraph on an existing one, then add new data sources like Snapshot votes, Discord role assignments (via a bot emitting events), or completion records from a project management tool like Dework. This creates a holistic view of a member's contributions. However, carefully validate any off-chain data sources to maintain the graph's trustworthiness. The final design should serve clear use cases: streamlining governance, incentivizing collaboration, and surfacing the latent structure of your DAO.

A DAO social graph is a data structure that models the relationships, roles, permissions, and reputations of its members. Unlike a traditional social network, its primary function is coordination—facilitating governance, task assignment, and resource allocation. Core components include nodes (members, roles, proposals) and edges (relationships like "votes-for," "belongs-to," or "has-permission"). Designing this graph requires balancing transparency for accountability with privacy for sensitive interactions. Common implementations use a hybrid approach: storing permissioned relationships on-chain (e.g., using ERC-20/ERC-721 for membership, ERC-1155 for roles) while keeping detailed social data off-chain, referenced via decentralized storage like IPFS or Ceramic.

Access control is the enforcement layer of your social graph. Smart contracts must gate actions based on a member's position within the graph. Use established standards like OpenZeppelin's AccessControl for role-based permissions (e.g., ADMIN_ROLE, CONTRIBUTOR_ROLE). For more dynamic, relationship-based permissions, consider a graph-based access control model. Here, a smart contract can query a verifiable credential or a zk-proof to confirm a relationship exists without revealing its details. For example, a proposal contract could allow voting only for members who have an "approved-by-curator" edge in an off-chain graph, with proof of this edge submitted on-chain.

Privacy is critical for sensitive coordination, like salary discussions or security vulnerability reporting. Techniques include: zero-knowledge proofs (ZKPs) to prove graph properties (e.g., "I am in the core-dev group") without revealing member identities; semaphore for anonymous signaling or voting; and fully homomorphic encryption (FHE) for private computation on encrypted graph data. Off-chain, use encrypted data stores with access keys managed by the graph's permission rules. A practical stack might use Lit Protocol for decentralized key management and access conditions tied to on-chain assets, allowing encrypted data in IPFS or Tableland to be decryptable only by verified graph neighbors.

For implementation, start by defining your core entities and relationship types in a schema. Use a graph database like GraphQL with The Graph for indexing on-chain events into queryable subgraphs, or GunDB for a decentralized real-time graph. A reference architecture involves: 1) On-chain Registry: ERC-721 NFTs for member souls, with token-bound accounts (ERC-6551) for roles. 2) Off-chain Graph: A Ceramic data stream defining edges (e.g., memberA --[REPORTS_TO]--> memberB). 3) Access Verifier: A smart contract that checks signatures or ZKPs against the off-chain graph state. 4) Client SDK: A library (e.g., using React and ethers.js) that fetches graph data and constructs valid permissions for transactions.

Consider these design patterns for common DAO functions. For reputation-weighted voting, create an edge like member --[HAS_REPUTATION]--> uint256_score, stored off-chain with a verifiable Merkle proof submitted with each vote. For task assignment and bounties, model edges like member --[HAS_SKILL]--> skill_tag and use a matching algorithm off-chain, with on-chain settlement. For private subgroups, implement a zk-group membership protocol where the existence of a shared secret (proven via ZKP) grants access to a private communication channel. Always audit the attack vectors: sybil attacks on reputation edges, collusion in private groups, and centralization risks in off-chain graph providers.

The final step is iterative testing and governance of the graph itself. Deploy a graph governance module that allows members to propose new relationship types or modify edge weights, with changes decided by the existing graph's stakeholders. Tools like OrbitDB can provide a CRDT-based graph for conflict-free decentralized updates. Monitor key metrics: proposal throughput, permission check gas costs, and data availability. A well-designed social graph transforms a DAO from a flat token-voting system into a resilient, nuanced organization capable of complex, private, and efficient coordination.

You have now explored the core components of a DAO social graph: on-chain identity (via ENS, Sign-In with Ethereum), relationship modeling (follows, contributions, delegations), and data indexing (using The Graph or Airstack). The next step is to integrate these pieces into a cohesive application. Start by defining a minimal viable graph (MVG) that tracks one key relationship, such as delegated_votes or co-contributed_to. Use a subgraph to index this data from your DAO's smart contracts and expose it via a GraphQL API for your frontend to query.

For practical implementation, consider these frameworks and tools. Lens Protocol offers a pre-built, composable social graph on Polygon, ideal for bootstrapping. For custom builds, use The Graph's subgraph studio to index events from your governance or reputation contracts. Airstack provides unified APIs for cross-chain social data, simplifying queries across Ethereum, Polygon, and Base. A basic frontend can use React with the Apollo Client to fetch and visualize graph data, mapping member influence and activity clusters.

The true power of your social graph emerges through analysis and iteration. Use the graph to surface insights: identify key influencers via centrality algorithms, detect collaboration clusters, or highlight under-engaged members with high potential. Tools like NetworkX in Python or Cytoscape.js in the browser can help analyze and visualize these patterns. Continuously refine your model based on DAO feedback—add new relationship types like mentorship or project_affiliation as needs evolve. Remember, the graph is a living representation of your community; its structure should adapt to serve better coordination, not dictate it.

Further your learning by exploring existing implementations. Study how Gitcoin Passport aggregates identity stamps, or how DAOstar's EIP-4824 standardizes DAO metadata. Engage with the Graph Academy for advanced subgraph development. The goal is to move from passive membership lists to an active, queryable map of human capital, enabling your DAO to coordinate with unprecedented efficiency and insight. Start small, validate with data, and iteratively build the social layer your community needs.