Abstract :

Self-Updating Link Prediction Using Dynamic Knowledge Streams Presents An Intelligent And Adaptive Framework For Forecasting Missing Or Future Connections In Complex Networks. Traditional Link Prediction Models Rely On Static Snapshots Of Network Data, Which Limits Their Ability To React To Rapidly Changing Information. To Address This Challenge, The Proposed Approach Continuously Integrates Dynamic Knowledge Streams—such As Real-time Interactions, Evolving Node Attributes, And Temporal Relationship Patterns—to Refine Prediction Accuracy. The System Employs Incremental Learning Techniques, Temporal Graph Analytics, And Adaptive Similarity Measures To Automatically Update Its Internal Representation Of The Network As New Data Arrives. This Enables The Model To Detect Emerging Connections, Strengthen Evolving Relationships, And Capture Previously Unseen Structural Shifts. Experimental Results Demonstrate That The Self-updating Mechanism Significantly Improves Prediction Performance Across Social, Biological, And Communication Networks While Reducing Model Retraining Overhead. Overall, The Framework Enhances Scalability, Responsiveness, And Reliability In Environments Where Relationships Evolve Over Time. Keywords: Dynamic Networks, Link Prediction, Incremental Learning, Temporal Graph Analytics, Adaptive Similarity Measures, Knowledge Streams, Network Evolution, Real-time Graph Updates, Self-updating Models, Complex Network Analysis.

Published:

11-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

123-129

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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