PageRank Social Authority

A framework for applying Google's PageRank concept to social networks, creating a weighted system of social connections and influence.

Core PageRank Concepts

• Two main ranking factors:
  • Content quality/context
  • Authority (determined by incoming links)
• Links pass authority proportionally
  • Higher authority sites pass more value
  • Value is divided among all outbound links
• Authority is recursive
  • Sites with high authority pass more value
  • Example: NY Times link worth more than personal blog

Applied to Social Networks

• Current Problem:

  • All follows have equal weight
  • Networks are symmetric/unweighted
  • No authority distribution system

• Proposed Solution:

  • Replace basic "follow" with weighted endorsements
  • Users can "buy" into others using cryptocurrency
  • Authority/influence passes proportionally based on buyer's own value
  • Creates natural authority distribution similar to PageRank

Key Mechanics

• Financial Endorsement

  • Users buy shares/coins of other users
  • Small amounts ($1-5) represent stronger signal than basic follow
  • Purchase amount indicates strength of endorsement

• Value Transfer

  • High-value users transfer more authority when endorsing
  • Similar to venture capital - investment from prestigious firm carries more weight
  • Authority distributes across network based on endorsement patterns

• Network Effects

  • Creates self-reinforcing system of authority
  • More valuable endorsers create more valuable endorsed
  • Natural weighting of influence emerges organically

This creates a more meaningful social graph where influence and authority are earned and distributed based on real endorsements rather than simple follow counts.