M
Madhav V. Marathe
Researcher at University of Virginia
Publications - 356
Citations - 15017
Madhav V. Marathe is an academic researcher from University of Virginia. The author has contributed to research in topics: Approximation algorithm & Computer science. The author has an hindex of 53, co-authored 315 publications receiving 13493 citations. Previous affiliations of Madhav V. Marathe include University at Albany, SUNY & Los Alamos National Laboratory.
Papers
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Book ChapterDOI
Using Network Reliability to Understand International Food Trade Dynamics
Madhurima Nath,Srinivasan Venkatramanan,Bryan Kaperick,Stephen Eubank,Madhav V. Marathe,Achla Marathe,Abhijin Adiga +6 more
TL;DR: This work analyzes international trade networks corresponding to four solanaceous crops obtained using the Food and Agricultural Organization trade database using Moore-Shannon network reliability and presents a novel approach to identify important dynamics-induced clusters of highly-connected nodes in a directed weighted network.
Book ChapterDOI
Effects of opposition on the diffusion of complex contagions in social networks: an empirical study
TL;DR: The empirical results point out how network structure and opposing perspectives can alter the widespread adoption of social behaviors that can be modeled as complex contagions.
Book ChapterDOI
Simulation Analytics for Social and Behavioral Modeling
TL;DR: The emerging field of simulation analytics is described in the context of social and behavioral modeling and a distributed and relational perspective on agency is discussed that has implications for the use of simulation platforms for understanding these phenomena.
Journal ArticleDOI
Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems
Abhijin Adiga,Chris J. Kuhlman,Madhav V. Marathe,S. S. Ravi,Daniel Rosenkranz,Richard Edwin Stearns,Anil Vullikanti +6 more
TL;DR: Using a discrete dynamical system model for a networked social system, this work establishes bounds on the number of queries needed to learn the local functions under both active query and PAC learning models.
Journal ArticleDOI
Budget constrained minimum cost connected medians
TL;DR: This paper formulation of the Budget Constrained Connected Median Problem as a bicriteria network design problem, and lower bounds on the approximability of the problem are proved which demonstrate that the performance ratios are close to best possible.