It happens with core computer science topics as well. The misconceptions around Big O notation, for example, are astonishing. O(f(n)) is the set of functions th

It happens with core computer science topics as well. The misconceptions around Big O notation, for example, are astonishing. O(f(n)) is the set of functions that asymptotically grow at most as fast as f(n). It's not limited to measuring running time in the RAM model, or somehow captures the variable n, or anything like that. You can use it to measure memory use, or communication volume, or anything else you like. It can be defined in multiple variables, like number of nodes and edges, or input size and approximation factor, or whatever else you might need. And all of n², n²+n log(n), √n log(n), 42, and 1/n are in O(n²) because O isn't Θ, ffs.</rant>