Missing link prediction and spurious link detection based on attractive force and community.
1School of Economics and Management, Harbin Engineering University, Harbin, China.
Science Progress
|May 21, 2021
Summary
This study introduces a novel network link prediction method to identify both missing and spurious links. The approach achieves higher accuracy and stability in real-world network analysis.
Area of Science:
- Network Science
- Information Technology
- Data Analysis
Background:
- Networks are crucial for global information diffusion, but increasing data scale poses challenges for link accuracy and completeness.
- Traditional link prediction methods often focus solely on missing links, neglecting spurious ones.
Purpose of the Study:
- To propose a novel link prediction approach that addresses both missing and spurious links in networks.
- To enhance the accuracy and reliability of network structure analysis.
Main Methods:
- Defining an 'attractive force' between node pairs to quantify relationship strength.
- Community detection based on node degrees and attractive forces.
- Calculating 'connection probability' for unconnected nodes to predict missing links.
- Calculating 'break probability' for connected nodes to detect spurious links.
Main Results:
- The proposed method effectively predicts missing links by analyzing connection probabilities.
- Spurious links are identified by evaluating break probabilities between connected nodes.
- Experimental results on real-world networks demonstrate superior prediction accuracy and stable performance over existing methods.
Conclusions:
- The novel approach offers a comprehensive solution for network link prediction, handling both missing and spurious links.
- The method provides a more accurate and stable way to analyze and understand complex network structures.
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