Social Graph Portability Pattern

Use Cases

• *Pattern Name**: Social Graph Portability
• *Source Platforms**: Facebook, LinkedIn, Twitter/X, Instagram
• *Date Identified**: 25-08-26 20:15
• *Pattern Status**: validated
• *Current State**: Users build valuable social graphs on platforms but cannot take their connections with them when migrating to new services, creating strong lock-in effects and reducing platform competition.
• *User Pain Points**: - Lost social connections when leaving platforms
• *Current Approach**: - Centralized social graph in TAO database
• *Strengths**: - Sophisticated relationship categorization (close friends, acquaintances)
• *Weaknesses**: - Complete vendor lock-in with no portability
• *Current Approach**: - Professional network focus with connection degrees
• *Strengths**: - Clear professional context for relationships
• *Weaknesses**: - Limited to professional contexts only
• *Current Approach**: - Asymmetric follow model vs symmetric connections
• *Strengths**: - Public by default encouraging open networking
• *Weaknesses**: - Lack of relationship context and categorization
• *Implementation**: Store rich context about relationships including:
• *Value**: Enables meaningful relationship management and appropriate privacy controls
• *Implementation**: Multi-layered privacy system allowing:
• *Value**: User agency over social graph visibility and sharing
• *Implementation**: Standards-based relationship portability:
• *Value**: Platform independence and user choice in social networking
• *Implementation**: Focus on meaningful connections through:
• *Value**: More valuable social networks and reduced noise
• *Anti-Pattern**: Platforms that make relationship export difficult or impossible
• *Facebook Example**: "Download Your Information" excludes critical relationship context and cross-platform data
• *Avoidance**: Provide comprehensive, real-time relationship export with full context and metadata
• *Anti-Pattern**: Complex, hidden privacy settings that users don't understand
• *Facebook Example**: 15+ privacy menus with contradictory and changing defaults
• *Avoidance**: Simple, transparent privacy controls with clear explanations and consistent defaults
• *Anti-Pattern**: Platform algorithms that manipulate social connections for engagement
• *Facebook Example**: "People You May Know" using invasive data collection and correlation
• *Avoidance**: User-controlled relationship discovery with transparent algorithms and data sources
• *Anti-Pattern**: Platforms that can unilaterally change relationship rules or access
• *General Example**: API restrictions, algorithm changes, or account suspensions affecting social graph
• *Avoidance**: User-owned social graphs with platform-independent relationship management
• *Relationship Ownership**: Clear messaging that users own their social graph
• *Privacy First**: Privacy-protective defaults with granular user control
• *Platform Choice**: Easy migration between PeerMesh-compatible services
• *Relationship Quality**: Tools for meaningful connection management
• *Phase 1**: Basic relationship storage and privacy controls
• *Phase 2**: Cross-platform import/export with major social networks
• *Phase 3**: Federated relationship discovery and verification
• *Phase 4**: Advanced relationship management and quality tools