Contributor Graph

The graph serves as a decentralized reputation system, quantifying a participant's influence and trustworthiness based on their historical actions. Key metrics include:

• On-chain contributions: Code commits, governance votes, and protocol upgrades.
• Social capital: Delegated votes, community moderation, and content creation.
• Economic alignment: Token holdings, staking duration, and liquidity provision. This creates a soulbound reputation layer that is portable across applications.

Projects use the Contributor Graph for merit-based airdrops and retroactive funding. Instead of simple token snapshots, rewards are weighted by a user's proven contribution score. For example:

• Optimism's RetroPGF uses attestations to fund public goods.
• Gitcoin Grants leverages quadratic funding, where a contributor's graph data can influence matching weight. This ensures capital flows to the most impactful ecosystem participants.

The graph powers advanced delegated governance models. Voters can delegate their voting power not just to individuals, but to delegation strategies defined by a contributor's graph attributes (e.g., "delegate to top-10 code contributors"). This enables:

• Context-aware voting: Weight votes based on expertise in specific protocol areas.
• Reduced sybil attack surface: Sybil clusters are identifiable via their shallow, inorganic graph patterns.
• Dynamic delegate discovery: Finding experts based on contribution history.

A core utility is identifying and mitigating Sybil attacks. By analyzing the graph's structure—such as transaction patterns, social connections, and contribution uniqueness—algorithms can detect clusters of fake identities. This is critical for:

• Fair launch mechanisms: Preventing whale manipulation of airdrops.
• One-person-one-vote systems: Ensuring democratic processes in DAOs.
• Grant allocation: Distributing funds to unique human contributors, not farms.

The graph acts as a talent marketplace and coordination tool. Projects can discover contributors with specific skill sets verified by on-chain activity. Uses include:

• Recruiting: Finding developers with a history of successful smart contract deployments.
• Team formation: Identifying complementary skill sets for grant proposals.
• Due diligence: Assessing the track record of teams before investment or delegation. This reduces information asymmetry in the decentralized labor market.

Analysts and DAOs use the Contributor Graph to measure ecosystem vitality. Key health indicators derived from the graph include:

• Contribution concentration: Gini coefficient of contributions to assess decentralization.
• Retention & churn: Rate at which new contributors become long-term participants.
• Cross-pollination: Movement of contributors between sub-projects, indicating healthy knowledge transfer. These metrics guide strategic decisions and resource allocation.

The persistent representation of an actor (e.g., wallet, smart contract) on a blockchain, forming the fundamental node in a Contributor Graph. Unlike traditional accounts, on-chain identity is pseudonymous and defined by its immutable transaction history.

• Key Attributes: Wallet address, transaction history, token holdings, and deployed contracts.
• Purpose: Serves as the atomic unit for graph analysis, linking activity across protocols and over time.

Frameworks for creating verifiable, portable claims about an identity's attributes or relationships, adding a social layer to the transactional Contributor Graph.

• Examples: Ethereum Attestation Service (EAS), Gitcoin Passport, Farcaster social graph.
• Function: Encode trust, reputation, and community membership as signed data, allowing graphs to model social capital and delegated authority beyond pure financial flows.

The set of mechanisms and analyses used to distinguish between unique human contributors and clusters of fake identities (Sybils) controlled by a single entity.

• Techniques: Graph clustering algorithms, analysis of funding sources, correlation of transaction patterns, and proof-of-personhood protocols.
• Critical For: Ensuring the integrity of governance, airdrop distributions, and reputation systems built on Contributor Graphs.

The quantitative methods applied to a Contributor Graph to extract insights about network health, influence, and behavior.

• Common Metrics: Degree Centrality (number of connections), Betweenness Centrality (control over flows), clustering coefficients, and community detection.
• Use Case: Identifying key opinion leaders, mapping capital flow between protocols, and detecting coordinated voter blocs in DAOs.

A primary application of Contributor Graphs, where the graph models membership, voting power, proposal creation, and delegation.

• Graph Role: Maps the governance graph—who votes, who delegates to whom, and how influence propagates.
• Analysis: Used to assess voter apathy, concentration of power, and the effectiveness of governance structures.

The verifiable record of the origin and history of the data used to construct a Contributor Graph, crucial for auditability and trust.

• Components: Source blockchain, specific block ranges, indexing methodology, and the raw event logs or transaction traces.
• Importance: Ensures the graph is a reproducible and tamper-evident representation of on-chain reality, not a manipulated dataset.