Concept spread results for 2 concept environments

Raw data for multiple concepts spreading within a network.

We have two concepts spreading, Concept 0, which is our target concept, and Concept 1 which is the concept that we can control.

Concept 0 selects its seeds randomly.

Concept 1 selects seeds based on chosen heuristic.

Heuristics have a number ID for ease of labelling files. The following numbers are used within this data:

0 - Random selection

2 - Single Discount

3 - Degree

9 - Degree Discount

16 - MPG

17 - MoBoo

File names follow this format:

[number of seeds]_[number of nodes]_[heuristic number]_[Relationship strength * 10]_[average LT threshold * 10]_[IC probability of infection * 100]_[burn in time]_[run number]_[network type]_[network characteristic]_[controllable Concept]Boost[target Concept].txt

So, for example, if we are performing run 38 on a 25000 node  small-world graph, with a seed set size of 100, clustering exponent of 0.25, burn in time of 2 time steps, the controllable concept selecting seeds using degree discount, both concepts using the independent cascade model of spread, with a probability of infection of 0.1, a LTM average threshold of 0.8 and relationship strength of 0.2 the file would be:

100_25000_9_2_80_10_2_38_SW_25_ICBoostIC.txt

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.