If I tried to match the word linkedin slightly modified (reversed or with '1' or '!' instead of 'i' like in l1nked1n):
In the first iteration, 558 passwords found in the 554,404 (0.1%) are related to the ‘Linkedin’ string;
In the second iteration, 3248 out of 22,688 (14%) are related to the ‘Linkedin’ string;
Third iteration: 1,733 out of 3,682 (47%);
Fourth iteration: 539 out of 917 (59%);
Fifth iteration: 217 out of 330 (66%);
Sixth iteration: 119 out of 152 (78%);
Seventh iteration: 40 out of 51 (78%);
And so on through the tenth iteration.
An example of what I found on the 7th pass is: m0c.nideknil
Another example is: lsw4linkedin, which was found on the tenth pass. To illustrate how the rules work for modifying words in the dictionary, below is the actual set of modifications used to get from the dictionary entry 'pwlink' to the successfully cracked password 'lsw4linkedin' over the ten iterations:
pwdlink from pwlink with the rule "insert d in 3rd position"
pwd4link from pwdlink with the rule "insert 4 in 4th position"
pwd4linked from pwd4link with the rule "append ed"
pw4linked from pwd4linked with the rule "remove 3rd char"
pw4linkedin from pw4linked with the rule "append in"
mpw4linkedin from pw4linkedin with the rule "prepend m"
mw4linkedin from mpw4linkedin with the rule "remove second character"
smw4linkedin from mw4linkedin with the rule "prepend s"
sw4linkedin from smw4linkedin with the rule "remove second character"
lsw4linkedin from sw4linkedin with the rule "prepend l"