Universally Useful Primitives for Aligning Networks Across Time and Space
Technical Report,01 Dec 2017,31 Jan 2020
University of Massachusetts Amherst Amherst United States
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Their team has, in the course of working on the DARPA Modeling Adversarial Activity MAA program, further developed a fast, flexible suite of graph matching tools designed to robustly align large networks in the presence of noise, paying special heed to developing methods for multiplex matching developed the theory and methodology behind Graph Matching Matched Filters which provide a principled, scalable method for discovering noisy subgraphs in a larger background graph provided an open source R code-base, denoted iGraphMatch, for implementing our graph matching and graph matching matched filters methods and their competitors at scale further developed the theory of vertex nomination, developing the analogues of the classical statistical concepts of consistency and Bayes optimality in the context of vertex nomination developed a novel concept of adversarial contamination and data-adaptive regularization inthe context of vertex nomination developed a suite of flexible vertex nomination algorithms designed to be implemented on large, noisy networks produced illustrative simulations and data analyses on MAA provided data and on externally provided real data sources.
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