Data for Concept spread results for 2 concept environments

In this paper, we consider the problem of indirect concept manipulation, and propose heuristics for indirectly boosting or inhibiting concept spread in environments where concepts interact. We define a framework that allows for the interactions between any number of concepts to be represented, and present a heuristic that aims to identify important influence paths for a given target concept in order to manipulate its spread.

We compare the performance of this heuristic, called maximum probable gain, against established heuristics for manipulating influence spread.<br><br>Data record consists of 2 zip archives, containing raw data for multiple concepts spreading within a network. Files follow this format: 10 line preamble listing the parameters of the run/runs Dashed line break "Run #" where # is the current run number Time in milliseconds of start "Timestep n" - n starting at 0 "Infections [conceptType]0: [Number of infected nodes in current time step for target]" "Infections [conceptType]1: [Number of infected nodes in current time step for controllable]" These three lines repeat for each time step.

Before a final result heading, with the final infections. Time in milliseconds that run ended. Dashed line divider.

Then, if the file contains a single run the file will end or, if it contains multiple runs, will proceed to the next run.