Project: #92 Influence maximization models for network interactions Progress Report - Reporting Period Ending: March 31, 2018 Principal Investigator: Daniel Lee Status: Active Start Date: None End Date: None Research Type: Advanced Grant Type: Research Grant Program: MAP-21 TSET National (2013 - 2018) Grant Cycle: 2017 TSET UTC Progress Report (Last Updated: March 31, 2018, 7:06 a.m.) % Project Completed to Date: 70 % Grant Award Expended: 0 % Match Expended & Document: 0 USDOT Requirements Accomplishments We show the influence of external thermal noise on influence models. Thus, we can relate influence maximization in these systems in terms of a physical model where the magnetization of an Ising system is maximized given a budget of external magnetic field. Using this theoretical formulation, we demonstrate analytically that for small external-field budgets, the optimal influence solutions exhibit a highly non-trivial temperature dependence, focusing on high-degree hub nodes at high temperatures and on easily influenced peripheral nodes at low temperatures. Impacts Understanding the effect of uncertainty and noise in social network models is critical. In a transportation system, interactions among humans could be effected by externalities due to weather and traffic conditions. Our analysis could eventually lead to better understanding of collective effects in such systems. Other Ising model analysis software can be provided. Outcomes New Partners N/A Issues None