Waveform Mapping for Semantic Network Evolution Tracking
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Solution Overview
Problem
Semantic networks, such as social networks, are complex and constantly evolving, making it difficult to track and predict their behavior over time due to their large number of nodes and edges, which existing technologies have not effectively addressed.
Innovation Solution
A waveform mapping technique and process that creates multiple sequential adjacency matrices using rules associated with a semantic network, generates waveforms from these matrices, and applies filtering and prediction techniques like the Kalman filter to estimate future evolution and behavior, allowing for the identification of organizational structures and effects of proactive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to track semantic networks, then the network structure can be represented, but the complexity of tracking and predicting behavior over time increases due to the large number of nodes and edges
Solution Approach 1:
The patent segments the complex semantic network tracking problem into distinct temporal phases by creating multiple sequential adjacency matrices, where each matrix represents the network state at a specific time point. This segmentation allows the system to manage complexity by processing the network in discrete, manageable units rather than as a continuous complex structure.
Solution Approach 2:
The patent transforms the network data from graph domain to waveform domain, adding a temporal dimension to the analysis. By mapping adjacency matrices to waveforms, the system converts complex network relationships into a different representation space that is more amenable to filtering and prediction operations.
2Loss of information
If sequential adjacency matrices are created to track network evolution, then temporal behavior can be analyzed, but the amount of data processing and computation increases
Solution Approach 1:
The patent introduces waveforms as an intermediary representation between the adjacency matrices and the final analysis results. The waveforms serve as a mediator that captures the essential temporal characteristics of the network evolution while being more efficient to process than the full adjacency matrices, thus reducing computation time while retaining important information.
Solution Approach 2:
The patent replaces direct manipulation of complex graph structures with waveform processing techniques. Instead of performing complex graph operations on sequential adjacency matrices, the system substitutes this with waveform generation and filtering operations, which are computationally more efficient and enable faster processing while preserving the essential temporal dynamics.
3Reliability
If waveform mapping is applied to filter noise, then useful information can be revealed, but the complexity of the analysis process increases
Solution Approach 1:
The waveform representation serves as an intermediary that simplifies the filtering process. By converting network data into waveform form, the patent enables the use of standard signal processing techniques to filter noise, which is more straightforward and reliable than attempting to filter noise directly in the graph domain, thus improving analysis reliability while managing process complexity.
Data Source
AI summary
In certain embodiments, a computer-implemented method includes accessing first and second data associated with a semantic network, the first data indicating a first plurality of nodes within the semantic network and a first plurality of relationships between the first plurality of nodes at a first time, and the second data indicating a second plurality of nodes within the semantic network and a second plurality of relationships between the second plurality of nodes at a second time. The method further includes generating a first waveform from the first data and a second waveform from the second data. The waveforms indicate an activity level of each of the nodes within the semantic network. The method further includes analyzing the semantic network using the generated first and second waveforms.