I have a weighted network dataset that looks like this: enter image description here

The prev and curr values contain webpages, and n is the number of times users went from the prev webpage to curr webpage. So, each data row is a directed weighted edge, with the (prev, curr) pair describing the edge and the n being its weight. The dataset was cut off at n=10 (all edges with n <10 were dropped).

A log-log plot of the edge-level traffic, n, looks as expected: enter image description here It has a minimum edge traffic of 10, and follows a power-law-like straight line on the log-log scale.

When I sum up the edge traffic n by prev webpages, to get outgoing traffic per webpage, I get the following log-log plot: enter image description here My question is about that little bump in the distribution at the top left. It occurs at outgoing traffic volume = 20, so my guess is that it's caused by cutting off the edge-level traffic n at 10. But if that's the case, why don't the frequency values immediately return to the power-law-like straight line followed by the rest of the data?
Here's a log(y) plot close-up of that bump in the distribution: enter image description here

I've tried cutting off the data at edge-levels n=100 and n=500, and got similar bumps in the distribution plots. enter image description here enter image description here enter image description here enter image description here

I'm guessing that smaller and smaller versions of these bumps repeat throughout these distributions at multiples of the n cutoff.

To summarize, my question is:
Why is the bump at the top left of these frequency distributions shaped like that? Why is it gradually going up instead of there being two disjoint downward-sloping straight lines?


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