Social Network Pattern Detection via Context-Aware Graph Matching
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Solution Overview
Problem
Conventional Social Network Analysis (SNA) techniques lack integration with pattern matching methods, making it difficult to perform context-based evaluations of social network data automatically and efficiently.
Innovation Solution
The Social Network Aware Pattern Detection (SNAP) system integrates social network analysis with graph pattern matching, allowing for dynamic, context-based detection of patterns within social networks by using pre-defined SNA metrics and graph pattern matching algorithms to identify and score matches in input graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional SNA techniques are used for visual analysis of social networks, then analysts can reason about individual actors or network structure, but the analysis process is manual and cannot automatically detect context-based patterns
Solution Approach 1:
The patent combines Social Network Analysis (SNA) with graph pattern matching techniques into a unified system. The SNA component provides context-based evaluation metrics while the pattern matching component automatically detects structural patterns. This merging allows the system to perform both automated detection and context-aware evaluation, resolving the contradiction between automation and precision.
Solution Approach 2:
The patent introduces an intermediary layer that connects pattern matching results with SNA context evaluation. This intermediary component takes detected patterns and evaluates them against SNA metrics to assign context-based scores, enabling automatic detection while maintaining evaluation precision through the mediating evaluation layer.
2Productivity
If SNA metrics are computed automatically for automated inspection, then repetitive analysis can be performed, but there is no integration with pattern matching to provide context-based evaluation
Solution Approach 1:
The patent creates a multi-functional system that performs both pattern matching and SNA metric computation within a single integrated framework. The system can detect patterns, compute SNA metrics, and evaluate results contextually all through one unified process, improving productivity while managing complexity through functional integration rather than separate systems.
3Measurement precision
If graph pattern matching is used to detect patterns in social networks, then automated pattern detection is achieved, but the system lacks context awareness to reduce false positives
Solution Approach 1:
The patent performs preliminary computation of SNA metrics before pattern matching occurs. By pre-calculating context-based evaluation metrics and storing them, the system avoids repeated computations during pattern detection, reducing false positives through context awareness while minimizing additional computation time through advance preparation.
Data Source
AI summary
Enabling dynamic, computer-driven, context-based detection of social network patterns within an input graph representing a social network. A Social Network Aware Pattern Detection (SNAP) system and method utilizes a highly-scalable, computationally efficient integration of social network analysis (SNA) and graph pattern matching. Social network interaction data is provided as an input graph having nodes and edges. The graph illustrates the connections and/or interactions between people, objects, events, and activities, and matches the interactions to a context. A sample graph pattern of interest is identified and/or defined by the user of the application. With this sample graph pattern and the input graph, a computational analysis is completed to (1) determine when a match of the sample graph pattern is found, and more importantly, (2) assign a weight (or score) to the particular match, according to a pre-defined criteria or context.