[duckqlz]

Comparing the first example against a similar guess based on intuition:

zuckerberg => investor(21%), mark(20%)

cuban => investor(3%), mark(%4)

Using google as a general guide to how often these words appear together

mark cuban => About 40,500,000 results on google

"mark cuban" => About 13,200,000 results on google

"mark" "cuban" => About 33,500,000 results on google

investor cuban => About 80,800,000 results on google

"investor cuban" => About 945 results on google

"investor" "cuban" => About 9,810,000 results on google

mark zuckerberg => About 41,700,000 results on google

"mark zuckerberg" => About 29,400,000 results on google

"mark" "zuckerberg" => About 35,700,000 results on google

investor zuckerberg => About 11,100,000 results on google

"investor zuckerberg" => About 479 results on google

"investor" "zuckerberg" => About 3,160,000 results on google

Considering the above results of how often the base words appear together and the added knowledge that Mark Cuban is more recognized for his investment activity than Zuckerberg I wonder how the relational scores are calculated by the game.

(Note: I realize this is nit-picking in an extreme sense but I found myself very interested in the underlying tech behind the game and this was part of my exploration so I thought I would share it with everyone else. Feel free to tear apart my methods I am still very interested in how the OP coded their solution)

[wakamoleguy]

I suspect this is because "cuban" has a lot of meaning in other contexts as well. If you see "cuban" out of context, one may think of Cuba or even sandwiches before thinking about Mark Cuban or other investors.

[zeta0134]

I'm irritated to learn that proper nouns are allowed. That's unusual for word games, and imho breaks the spirit of the thing. But honestly most of the frustration is not knowing whether the game is going to treat two words as related enough in advance. It doesn't feel like I'm being clever, it feels like I'm blindly exploring a graph.