CyberConnect is a decentralized social graph protocol that helps dApps bootstrap network effects and build personalized social experiences. The decentralized protocol allows users to own their social connections while providing a composable social data layer for developers.

CyberConnect describes a data standard, storage infrastructure, identity, and permissions management mechanism as the foundation of a seamless and secure digital social experience in Web3. CyberConnect also proposes the topology of a social data network of indexed data and recommendations that can be universally accessed and permissionlessly contribute to. Incentive mechanisms are designed to grow the protocol into a thriving self-sustaining economy with expanding utility.

Identity

By adopting a decentralized social graph, users aggregate their identities across different platforms and applications. This allows developers to retrieve more comprehensive data related to a user's address, such as:

How does it work?

The indexer layer of the CyberConnect Protocol collects on-chain data from IPFS and blockchain networks (e.g. Ethereum, Solana) as well as off-chain data from web2 social platforms. The indexer then transforms and stores the raw data into graph databases, which enables it to curate user identities after deployment.

How does it work?

Let's take "follow" as an example. A user clicking the "follow" button in an application invokes the "connect" function in the CyberConnect JavaScript library. If this is their first connection in the current session, they need to manually sign the message. The CyberConnect library then generates and stores a derived key to authorize writing connection data onto IPFS and Arweave storage.

Recommend

An interoperable social graphs opens up the possibility for developers to better leverage data insights and more accurately recommend potential connections to their users. The recommender layer of the CyberConnect Protocol enables several types of algorithms to retrieve lists of recommended users:

• Asset-based weighted recommendation.

• Connection-based weighted recommendation.

• Unweighted recommendation (by overall popularity).

• Customizable recommendation.

Developers can incorporate notification modules to diversify the user experience by using the CyberConnect JS SDK and CyberConnect API.