#92 Influence maximization models for network interactions
- Principal Investigator
- Daniel Lee
- Status
- Completed
- Start Date
- None
- End Date
- None
- Project Type
- Research Advanced
- Grant Program
- MAP-21 TSET National (2013 - 2018)
- Grant Cycle
- TSET - University of Pennsylvania
- Visibility
- Public
Abstract
In large systems such as transportation networks, strategic interactions among agents can be analyzed using the tools of statistical mechanics in physics. Each agent interacts directly with only his/her neighbors, but the system is influenced indirectly through a chain of direct interactions. We have used a statistical mechanics models, the Ising model, to analyze and understand which agents have the most influence on the network. This work is an application of both game theory and network science to understand the overall behavior of a highly interacting network.
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Individuals Involved
| Name | Affiliation | Role | Position | |
|---|---|---|---|---|
| ddlee@seas.upenn.edu | Lee, Daniel | University of Pennsylvania | PI | Other |
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Amount of UTC Funds Awarded
$Total Project Budget (from all funding sources)
$Documents
| Type | Name | Uploaded |
|---|---|---|
| Publication | Statistical mechanics of influence maximization with thermal noise | March 30, 2018, 8:45 a.m. |
| Presentation | Surges of Collective Human Activity Emerge from Simple Pairwise Interactions | March 30, 2018, 8:45 a.m. |
| Progress Report | 92_Progress_Report_2018-03-31 | March 30, 2018, 8:45 a.m. |
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