Discussed in networkx/networkx#4918

There are Four Centrality Measures not found in NetworkX, Leverage Centrality, Bridging Centrality, Laplace Centrality, and Communicability. It would be useful if there are guides on including these into NetworkX.

def Leverage_Centrality(g):
    DegreeI = g.degree(nbunch = i)
    NeighboorsI = [n for n in g.neighbors(i

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.

We need to move the execution of CLI scripts used to build the documentation into a Make file. Wise suggestion from @louisguitton: DerwenAI/pytextrank#159 (comment)

Feature description (Mandatory)

Please add the capability to specify the type of array instead of defaulting to string. i.e.
apoc.array.int()

How this feature can improve the project?

By default, it is created as a string. Some customers have asked for the ability to hint at the data type.

The igraph tutorial (here online, source file here) should be reviewed:

• Is it up to date?
• Is it understandable by a newcomer?
• Are essential points unexplained or missing?

Ideally, this should be done by someone who is new to using igraph, to get a fresh perspective. Th

I have a gh graph on 37700 vertices and 289003 edges.

Calculation of its diameter like this:

maximum(maximum(gdistances(gh, i)) for i in vertices(gh))

takes around 250 seconds (probably this is not an optimal algorithm for the task but at least it works).

However, when I run diameter(gh) the process takes so long that I did not wait till it finished.