Simulations show information overload decreases source localization effectiveness in networks, with Erdős-Rényi graphs more resilient than Barabási-Albert ones and a reversal where less dense networks perform better under strong overload.
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Improper use of test data during hyperparameter tuning in link prediction inflates performance estimates by an average of 3.6 percent across 60 networks, as measured by a new Loss Ratio metric.
PATCH model simulations show preferential attachment and homophily increase segregation and degree inequality while triadic closure reduces segregation but amplifies overall inequality, and the model accounts for observed gender disparities in 50 years of physics and CS collaboration networks.
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Nonlinear dynamics of information overload: Impact on source localization in complex networks
Simulations show information overload decreases source localization effectiveness in networks, with Erdős-Rényi graphs more resilient than Barabási-Albert ones and a reversal where less dense networks perform better under strong overload.
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Impacts of Data Splitting Strategies on Parameterized Link Prediction Algorithms
Improper use of test data during hyperparameter tuning in link prediction inflates performance estimates by an average of 3.6 percent across 60 networks, as measured by a new Loss Ratio metric.
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Network Inequality through Preferential Attachment, Triadic Closure, and Homophily
PATCH model simulations show preferential attachment and homophily increase segregation and degree inequality while triadic closure reduces segregation but amplifies overall inequality, and the model accounts for observed gender disparities in 50 years of physics and CS collaboration networks.