Dynamic Social Network Analysis via Time-Decayed Contact Strength
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Solution Overview
Problem
Conventional methods for analyzing communication networks focus on static snapshots, failing to capture the dynamic evolution and interconnectedness over time, which limits understanding of a participant's personal network and social activity.
Innovation Solution
Representing contacts between participants and communication partners as a time sequence order, determining instant strength values, and charting the direction of personal network extent by monitoring changes in these values over time, using a system with a contact representation unit, personal network extent determination unit, and charting unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional network snapshot methods are used, then analysis simplicity is maintained, but dynamic evolution and temporal sensitivity of the network are lost
Solution Approach 1:
The patent transforms static network snapshots into dynamic temporal sequences by ordering contacts chronologically and calculating time-decayed strength values. This allows the system to capture evolving network structures while maintaining computational feasibility through systematic temporal analysis.
Solution Approach 2:
The patent adds a temporal dimension to traditional network analysis by representing contacts as ordered sequences and introducing time-based strength decay. This transforms two-dimensional network snapshots into three-dimensional temporal network structures, enabling detection of evolutionary patterns without excessive complexity.
2Measurement precision
If time sequence representation is implemented, then dynamic network evolution is captured, but computational complexity increases
Solution Approach 1:
The patent introduces a time decay parameter that systematically reduces the weight of older contacts. This parameter change transforms the complex problem of analyzing all historical contacts into a manageable calculation where recent contacts dominate, providing precise evolution detection with controlled computational complexity.
Solution Approach 2:
The patent maintains continuous temporal tracking of contact strength by updating strength values incrementally as new contacts arrive. This continuous update approach avoids reprocessing entire contact histories, enabling precise dynamic analysis with efficient incremental computations.
3Measurement precision
If contact strength is calculated using time decay, then recency and frequency of interactions are reflected, but calculation complexity increases compared to simple counting
Solution Approach 1:
The patent uses exponential or linear time decay parameters to weight contacts based on age and frequency. This parameter-based approach provides precise interaction strength measurement that captures both recency and frequency effects, while keeping calculations manageable through systematic parameter application rather than complex algorithms.
Data Source
AI summary
Determining the extent of a personal network and/or social activity of a participant in a communication network. On method includes: representing contacts between the participant and communication partners of the participant as a time sequence order; determining an instant strength value over time using the representation of contacts as a time sequence order; and charting a direction of the extent of the personal network and/or social activity of the participant by monitoring a change in the instant strength value over time.