Leaving this issue here for newcomers who might want to contribute.

@dschult to address the final point:

What about having a function from_node_dataframe?

I like the idea! I think it could be left for another newcomer to contribute, since I think the pattern is similar:

def from_node_dataframe(df):
    G = nx.Graph()
    for n, d in df.iterrows():
        G.

steps to reproduce

Write a loop, from 1 to 80000, each time add a random int to the max heap.

In theory it takes very little time(NlogN, N=80000, <1sec ), but the program does take a long time.

I'v also tested the BinaryHeap in https://github.com/SolutionsDesign/Algorithmia, it performs well, so it is probably due to the bad algorithm.

There are multiple graph generators that currently do not check their input for validity.

These need to be fixed:

• callaway_traits_game
• establishment_game

The following functions have been verified to check their arguments properly:

forest_fire_game

Please update the lists above as you verify functions or find new ones with problems.

This is marked as

It would be handy for all edge operations to have both f(g, e) and f(g, src(e), dst(e)) defined by default on abstract graphs