> For the complete documentation index, see [llms.txt](https://chainedx.gitbook.io/chainedx-protocol/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://chainedx.gitbook.io/chainedx-protocol/1.-introduction.md).

# 1. Introduction

<mark style="color:yellow;">Most people have come</mark> to rely predominantly on private platforms serving as trusted third parties to manage and facilitate communication with their broadest network of relationships. <mark style="color:yellow;">This network is commonly known as a “social graph,”</mark> and presently most social graphs are privately owned and controlled by a small number of large technology companies. While the current ecosystem of social graphs has pioneered a new way for people to interact with their personal network and public figures on an unprecedented scale, it suffers from inherent weaknesses related to trust, incentive models, and equitable participation in the attention economy.

These large, corporate-owned social graphs have made it possible for people to expand their network of relationships to an extremely large scale. However, each social graph is isolated, locking in users as a result of the high cost of re-creating a social graph in another provider’s “walled garden.” This balkanized environment also prevents the broader developer community from helping to address challenging problems or contributing new innovations to improve this pervasive technology.&#x20;

All major social networks employ personalization algorithms to determine what content is presented to users. These are centralized, usually closed algorithms that are opaque and proprietary, and they offer the user limited input or insight into how they operate. Through passive data collection, these algorithms often trigger users’ negative, fight-or-flight reactions to increase engagement.

This approach, in turn, creates more advertising opportunities and drives more revenue to the large companies that control the largest social graphs. Malicious actors exploit these algorithms by deploying bots, artificial profiles, and divisive content to manipulate users at scale.&#x20;

<mark style="color:yellow;">What is needed is a universally accessible, unified, and decentralized social graph that allows developers to build an ecosystem with a variety of applications.</mark> By decoupling applications and data, this ecosystem will allow for a wide range of personalization algorithms to be developed and employed by different applications, and even applied to specific communities and topics.&#x20;

This new approach will enable users to move seamlessly between applications without rebuilding their network of friends and public figures at each destination. Further, users will be able to choose from a diverse set of recommendation and moderation systems. Unbundling the social graph will lower switching costs and allow for a marketplace <mark style="color:yellow;">where developers compete on a level playing field, with diverse winners based on user needs and preferences</mark>.&#x20;

In this whitepaper, we propose a solution to the balkanization of social graphs by employing <mark style="color:yellow;">a public protocol for writing and reading social graph data</mark> on <mark style="color:yellow;">public blockchains using smart contracts</mark>. We include both a structure for the graph as well as the mechanism for creating authentic claims from and about the user. Further, we outline an approach to sharing content over such a social graph, and ways in which <mark style="color:yellow;">new services can be built</mark> to efficiently aggregate and disseminate such information on public blockchains at the scale required for a universal decentralized graph to be adopted by the majority of the population.


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