Probability Server System for Social Network Community Detection

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Solution Overview

Problem

Current social network analysis is limited to identifying friendships and information propagation, failing to effectively determine community structures and information flow based on user involvement levels.

Innovation Solution

A probability server system that calculates the likelihood of link formation between nodes in a social graph using active and passive involvement parameters, modeling information propagation and social ties through a stochastic mixture membership generative model to identify overlapping communities and determine the direction of information flow.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional social network analysis methods are used to identify friendships and information propagation, then basic social relationships can be detected, but community structures and information flow patterns cannot be effectively determined

Engineering Contradiction:

Improvecommunity detection accuracyVSAvoidinformation about user involvement levels

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent introduces an intermediary probabilistic model that mediates between observed social network data and underlying community structures. This model acts as a bridge, inferring latent community memberships and involvement levels from observable friendship patterns and information propagation events, thereby recovering information that is not directly observable in traditional analyses

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional deterministic mechanical analysis methods with a probabilistic statistical model. Instead of using fixed rules to determine community membership, the system employs probability distributions to model uncertainty and infer hidden structures, allowing for more nuanced detection of community boundaries and user involvement levels

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If a simple friendship-based model is used, then basic social ties can be identified, but overlapping communities and information propagation patterns cannot be captured

Engineering Contradiction:

Improvecommunity overlap detectionVSAvoidmodel complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces dynamic probabilistic parameters that allow community memberships and involvement levels to vary across different contexts and time periods. The model can adapt to changing social structures by updating probability distributions based on new observations, enabling detection of evolving overlapping communities without requiring a completely different analytical framework

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the fundamental parameters of the analysis from binary friendship indicators to continuous probabilistic measures of involvement. By transforming discrete social data into continuous probability distributions, the model can capture nuanced variations in user engagement and naturally represent overlapping community memberships through parameter variations rather than structural complexity

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If detailed information propagation data is collected, then information flow patterns can be analyzed, but the complexity of processing and interpreting this data increases significantly

Engineering Contradiction:

Improveinformation propagation patternsVSAvoiddata processing complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent extracts only the essential probabilistic features from detailed propagation data, separating signal from noise. By focusing on key parameters such as propagation timing, recipient responses, and path patterns, the model extracts meaningful information flow characteristics without requiring processing of every detailed interaction, thereby reducing computational complexity while preserving essential patterns

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent performs preliminary probabilistic modeling and feature extraction before full analysis. By pre-processing propagation data to identify likely community memberships and involvement levels in advance, the system reduces the complexity of subsequent interpretation steps, organizing raw propagation data into structured probabilistic representations that are easier to analyze

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS9342854B2Identifying communities within a social network based on information propagation data

Publication Date: 2016.05.17 R2 SOLUTIONS LLC

AI summary

Methods and systems for identifying communities based on information propagation data are described. One of the methods includes receiving a social graph, which includes nodes and relationships between the nodes. The method further includes receiving a number of the communities to find within the social graph, receiving data regarding propagation of information between the nodes, and calculating a probability of formation of a link between a first one of the nodes and a second one of the nodes based on the data. The link provides a direction of flow of media between the first and second nodes. The method includes calculating a probability that media will be accessed by the second node based on the data. One of the communities includes the first node, the second node, and the link.