Contrastive Graphing for User Segmentation
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Solution Overview
Problem
Conventional graphing systems fail to effectively identify and track communities of entities in entity networks by neglecting temporal-spatial information and entity personas, leading to inaccurate user segmentation and content customization.
Innovation Solution
A contrastive graphing system that uses contrastive learning to generate node embeddings and cluster assignments, considering hierarchical community structures and network homophily, enabling accurate segmentation of users and adaptation to changing community structures through incremental learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional graphing systems are used to identify similar groups of users, then the system structure remains simple, but the user segmentation accuracy deteriorates due to neglecting temporal-spatial information and entity personas
Solution Approach 1:
The patent segments the graph data processing into multiple components: temporal-spatial information extraction, entity persona identification, and graph clustering. This segmentation allows each component to be optimized independently, improving overall user segmentation accuracy while managing system complexity through modular architecture
Solution Approach 2:
The patent introduces temporal-spatial dimensions to traditional graph analysis by incorporating time-stamped interaction data and spatial relationships. This adds new dimensions to the feature space, enabling more accurate user segmentation by considering when and where interactions occur, not just who interacts with whom
2Reliability
If conventional graphing systems are used, then the system is easy to operate, but the ability to detect community evolution and structure changes deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the graph clustering results are continuously refined based on temporal-spatial information and entity personas. The system uses iterative optimization where clustering outcomes feed back into the feature extraction process, improving community detection accuracy through continuous refinement
Solution Approach 2:
The patent performs preliminary extraction of temporal-spatial information and entity personas before conducting graph clustering. This preliminary action prepares enriched feature representations that significantly improve community detection accuracy, as the complex processing is done in advance rather than during the clustering operation
3Measurement precision
If conventional graphing systems are used, then the system requires less computational resources, but the content customization accuracy deteriorates
Solution Approach 1:
The patent applies local quality by extracting and utilizing specific temporal-spatial features and entity persona characteristics that are most relevant to each user's content customization needs. Rather than processing all possible features uniformly, the system focuses computational resources on the most discriminative local features, improving content customization accuracy while managing energy consumption
Data Source
AI summary
Systems and methods for contrastive graphing are provided. One aspect of the systems and methods includes receiving a graph including a node; generating a node embedding for the node based on the graph using a graph neural network (GNN); computing a contrastive learning loss based on the node embedding; and updating parameters of the GNN based on the contrastive learning loss.