Analyzing Connections in Social Networks

This patent describes systems for analyzing how people are connected in online communities by assigning scores for things like trust or influence, then using those scores to make automated decisions.

What it covers

This system analyzes connections within a network community by assigning specific 'user connectivity values' to members. These values can represent factors such as 'alignment, reputation, status, and/or influence' or 'the degree of trust' (Abstract). These values can be manually assigned by users or automatically generated based on interactions or third-party data (Abstract). The system then finds 'paths connecting a first node to a second node' and performs 'social graph data analytics' on these paths (Abstract). Finally, the analyzed connectivity values and other social graph data are 'outputted to third-party processes and services' to help with 'initiating automatic transactions or making automated network-based or real-world decisions' (Abstract). For example, an online community platform could use this to automatically recommend a mentor to a new member based on high 'alignment' and 'reputation' scores.

What it doesn't cover

• —Does not cover analyzing non-social graphs, such as electrical circuits or transportation routes, as it specifically focuses on 'social graph data analytics'.
• —Does not cover systems that only visualize social connections without assigning specific 'user connectivity values' like reputation or trust.
• —Does not cover systems where decisions based on connectivity are made manually by a human, as it emphasizes 'initiating automatic transactions or making automated network-based or real-world decisions'.
• —Does not cover simple friend lists or follower counts without further analysis of 'alignment, reputation, status, and/or influence' among members.

The clever bit

The novelty lies in assigning diverse and specific 'user connectivity values' like alignment, reputation, status, influence, or trust, and then using these detailed metrics, derived from interactions or third parties, to drive automated decisions and transactions within a network.

Why it matters

Understanding the complex web of social connections is crucial for online platforms. This technology helps platforms manage communities, suggest relevant connections, and personalize user experiences. It influences how information spreads, how trust is established, and how effective online interactions can be.

Real-world examples

• 1.Social media platforms like Facebook or Instagram
• 2.Professional networking sites like LinkedIn
• 3.Online gaming communities
• 4.Online review platforms that rank users or contributions
• 5.Customer relationship management (CRM) systems analyzing customer influence