Graph Embedding for User Activity Metric Modeling
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Solution Overview
Problem
Current methods for analyzing user activity data rely on metric averages that fail to capture the complexity and interdependence of user actions, ignoring structural relationships and temporal dynamics, leading to inadequate insights and difficulty in diagnosing specific causes or inferring relationships between high-level metrics and low-level qualities.
Innovation Solution
The method involves collecting and processing data to generate graph data representing actors and objects, using graph embedding to convert this data into metric space, allowing for the modeling of higher-level metrics and revealing actionable insights into activity structure and dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If attribute-based aggregations (e.g., SLIMS, counts, or averages) are used to analyze collected data, then the analysis process is simple, but the interdependence of users' actions and the importance of structural relationships are lost
Solution Approach 1:
The patent transforms flat attribute-based data into multi-dimensional graph structures where nodes represent users/actions and edges represent relationships. This dimensional transformation preserves structural information that would be lost in traditional aggregations while enabling new types of analysis that consider both individual attributes and relational context.
Solution Approach 2:
The patent changes the fundamental parameters of data representation from simple attributes to graph-theoretic properties (nodes, edges, paths, communities). This parameter transformation allows the system to capture complex interdependencies and structural relationships that cannot be expressed through traditional metric aggregations.
2Productivity
If traditional metric averages are used to analyze user activity, then the analysis is computationally efficient, but the complexity and interdependence of user actions are not captured
Solution Approach 1:
The patent segments user activity data into discrete graph components (nodes representing individual actions or users, edges representing relationships between them). This segmentation allows for targeted analysis of specific structural patterns while maintaining computational efficiency through graph algorithms that can process large-scale data without requiring exhaustive computation of all possible relationships.
Solution Approach 2:
The patent introduces graph structures as an intermediary representation layer between raw user activity data and final analytics. This intermediary graph model captures complex interdependencies in a computationally tractable form, enabling precise measurement of user activity patterns without the exponential computational cost of analyzing all raw interactions directly.
3Measurement precision
If graph data is generated and converted through embedding processes, then higher-level metrics and structural relationships are captured, but the processing complexity and runtime costs increase
Solution Approach 1:
The patent performs graph embedding transformations in advance to convert complex graph structures into compact vector representations. This preliminary action pre-computes the structural relationships and stores them in an optimized format, allowing downstream analytics to operate on simplified vector data rather than reconstructing complex graph structures repeatedly, thereby reducing runtime processing complexity.
Solution Approach 2:
The patent creates vector space copies of graph structures through embedding processes. These vector copies preserve the essential structural relationships and metric properties of the original graphs while occupying significantly less computational space and enabling faster manipulation. The embedding vectors serve as efficient proxies that capture higher-level metrics without requiring the full graph structure to be present during analysis.
Data Source
AI summary
Systems and methods for modeling higher-level metrics from graph data derived from already-collected but not yet connected data are disclosed. A method includes extracting a first set of actor-related data, a second set of object-related data, and a third set of temporal data from the set of the already-collected but not yet connected data representative of a unit-level contribution to the target activity. The method further includes generating graph data for a graph using the set of the already-collected but not yet connected data, where each of the plurality of nodes of the graph corresponds to the actor or the object, and where an attribute associated with each of the plurality of edges of the graph corresponds to a measurement associated with the target activity. The method further includes modeling a relationship between graph attributes associated with the graph data and a higher-level metric associated with the target activity.