Tensor Factorization for Social Network Content Analysis
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Solution Overview
Problem
Existing methods for analyzing social networks fail to effectively integrate content and network dimensions, often treating them in isolation and ignoring the time dimension, leading to suboptimal performance and quality in understanding evolving social communication patterns.
Innovation Solution
A system that models social communication networks as multi-mode tensors, allowing for the construction of reduced dimensional representations and identification of clusters across content and network modes, enabling the extraction of interpretable patterns and visualizations that capture the evolution of social communication networks over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If content-based analysis and social network analysis are treated separately in isolation, then each analysis can be performed using dedicated methods, but ad-hoc post-processing is required to integrate results, causing deterioration of performance and quality
Solution Approach 1:
The patent merges content-based analysis and social network analysis into a unified tensor factorization framework. The tensor model simultaneously processes content dimensions (documents, terms) and network dimensions (users, interactions), eliminating the need for separate analyses and ad-hoc integration. This unified approach resolves the contradiction by achieving both ease of analysis through a single integrated method and high quality through joint optimization of content and network factors.
Solution Approach 2:
The tensor factorization model serves multiple functions simultaneously: it performs content analysis, network analysis, and their integration in a single unified framework. The model can analyze documents, terms, users, and their interactions together, making it universally applicable to both content-based and network-based analysis tasks without requiring separate dedicated methods.
2Measurement precision
If traditional content analysis methods are used, then document-term relationships can be identified, but the time dimension and network evolution are ignored
Solution Approach 1:
The patent extends traditional content analysis by adding network dimensions and time dimensions to the analysis framework. The tensor model incorporates user dimensions, interaction dimensions, and time dimensions alongside traditional document and term dimensions, enabling precise identification of relationships while simultaneously capturing network evolution and temporal dynamics.
Solution Approach 2:
The tensor factorization model is designed to be dynamic, capturing how content and network relationships evolve over time. The model can analyze temporal patterns in user interactions, document creation, and relationship formation, making it adaptable to time-evolving social networks while maintaining precision in relationship identification.
3Device complexity
If social network analysis focuses only on pair-wise associations, then network structure can be analyzed, but content context and time evolution are ignored
Solution Approach 1:
The patent combines pair-wise network association analysis with content analysis and temporal analysis in a unified tensor framework. The model simultaneously processes user interactions, document content, and time information, preventing loss of any dimension while maintaining a coherent integrated analysis approach rather than separate complex analyses.
Solution Approach 2:
The tensor model acts as a composite analytical framework that integrates multiple types of information (network structure, content, time) into a unified representation. This composite approach preserves all information dimensions while providing a cohesive analysis model that is more powerful than simple pair-wise analysis alone.
Data Source
AI summary
System and method for modeling a content-based network. The method includes finding single mode clusters from among network (sender and recipient) and content dimensions represented as a tensor data structure. The method allows for derivation of useful cross-mode clusters (interpretable patterns) that reveal key relationships among user communities and keyword concepts for presentation to users in a meaningful and intuitive way. Additionally, the derivation of useful cross-mode clusters is facilitated by constructing a reduced low-dimensional representation of the content-based network. Moreover, the invention may be enhanced for modeling and analyzing the time evolution of social communication networks and the content related to such networks. To this end, a set of non-overlapping or possibly overlapping time-based windows is constructed and the analysis performed at each successive time interval.