Related Experiment Video
Updated: Feb 16, 2026
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
1.2K
Measuring the value of accurate link prediction for network seeding
1Center for Computational Engineering, MIT, Cambridge, MA USA.
Computational Social Networks
|December 22, 2017
Summary
Influence maximization strategies using imperfect social network data can be valuable. Strategic investment in link prediction depends on the spread model, sometimes yielding significant gains in cascade size, while other times being wasteful.
Area of Science:
- Social network analysis
- Information diffusion modeling
- Computational social science
Background:
- Influence maximization aims to identify key individuals (seed sets) for widespread behavior cascades.
- Real-world social networks often have incomplete or inaccurate link information.
- Existing seeding strategies typically assume perfect network topology knowledge.
Purpose of the Study:
- To evaluate the effectiveness of seeding strategies when network topology is imperfectly known.
- To introduce a metric, optimized-against-a-sample (OAS), for assessing performance with noisy network data.
- To determine the conditions under which investing in link prediction is beneficial for influence maximization.
Main Methods:
- Developed the optimized-against-a-sample (OAS) performance metric.
- Conducted computational studies using synthetic and real-world networks.
- Investigated various threshold-spread models to analyze the impact of imprecise link information.
Main Results:
- The value of optimizing seeding strategies with noisy link data varies significantly.
- Strategic investment in link prediction effectiveness is highly dependent on the specific spread model.
- In certain parameter ranges, improved link prediction can substantially increase cascade size; in others, it offers no benefit.
Conclusions:
- Imprecise network information can still yield valuable insights for influence maximization.
- The optimal level of investment in link prediction is context-dependent, particularly on the spread model.
- Understanding the interplay between network noise and spread dynamics is crucial for effective influence maximization strategies.
Related Concept Videos
01:03Prediction Intervals
3.4K
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.
3.4K
00:59Propagation of Uncertainty from Random Error
2.0K
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...
2.0K