A novel complex network link prediction framework via combining mutual information with local naive Bayes
Zengqiang Chen1, Runfang Wang1, Zhongxin Liu1
1College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
Chaos (Woodbury, N.Y.)
|November 30, 2019
Summary
This study introduces a new algorithm for link prediction in complex networks. It improves accuracy by considering interactions within neighbor sets, not just common neighbors.
Area of Science:
- Complex Networks
- Data Mining
- Network Science
Background:
- Link prediction is crucial in complex networks and data mining.
- Previous methods focused on common neighbors but ignored their interactions.
- Interactions within entire neighbor sets influence link formation.
Purpose of the Study:
- To develop a novel approach for link prediction.
- To quantify and balance contributions from common neighbors and neighbor set interactions.
- To enhance the accuracy of link prediction.
Main Methods:
- Utilized local naive Bayes and mutual information to quantify influences.
- Introduced an adjustable parameter to balance contributions.
- Proposed the mutual information-based local naive Bayes algorithm.
Main Results:
- The proposed algorithm was tested on 5 diverse datasets.
- Performance was evaluated using 9 comparison indexes.
- Numerical simulations confirmed the algorithm's effectiveness.
Conclusions:
- The mutual information-based local naive Bayes algorithm significantly improves link prediction performance.
- Considering neighbor set interactions is vital for accurate link prediction.
- The method offers a more comprehensive approach to understanding link formation in networks.
Related Concept Videos
01:03Prediction Intervals
3.0K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.0K
01:07End Point Prediction: Gran Plot
1.1K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
1.1K
02:26Protein Networks
4.4K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.4K
02:26Protein Networks
2.7K
2.7K
01:30Classification of Signals
1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
00:59Propagation of Uncertainty from Random Error
1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K