# Graph library recommendations for large graphs

Diez B. Roggisch deets at nospam.web.de
Tue Aug 25 00:21:05 CEST 2009

```VanL schrieb:
> I am working on a project that will require building and querying large
> graph objects (initially 8M nodes, 30-40M edges; eventually 40M nodes,
> 100M edges). NetworkX seems to be the most popular, but I am concerned
> that a dict representation for nodes would use too much memory -- my
> initial tests suggest that a graph with 3M nodes and 12M edges creates
> substantial memory pressure on my machine.
>
> Can anybody who has worked with large graphs before give a recommendation?

My initial tests show otherwise. The below test-script creates 3 million
nodes with 12 million adjacencies, on my 2GB Notebook.

The theoretical limit for this (if we assume pointer-based
numbers indicate) is (32 bits assumed):

- 8 bytes per node (4 byte pointer to adjacency list, 4 byte int for
counting the number of adjacencies in that list)

This is 60.000.000 for your example - roughly 60MB. On my machine, the
process has 320.000.000MB - (roughly) a factor five. Given the much
richer properties a Python-object (and python-lists) have thas is pretty
good I'd say.

So for your eventual size of 40M nodes, 100M edges, we have a
theoretical amount of 560MB, times 5 makes 2.5 GB. Not exactly a low
memory profile, but manageable on modern hardware.

I don't know anything about NetworkX - it still might be the better
solution, given the underlying C-based algorithms. But if all you need
is to represent a graph of that size, it appears to be working.

---- test.py ----

import random
import gc
import time

class Node(object):

def __init__(self, id):
id = id
value = random.random()

nodes = []

gc.disable()
nc = 3000000

for i in xrange(nc):
nodes.append(Node(i))
if (i % 1000) == 0:
print i

for i in xrange(12000000):
a = random.randint(0, nc - 1)
b = random.randint(0, nc - 1)
while a == b:
b = random.randint(0, nc)
if (i % 1000) == 0:
print "e", i

gc.enable()
while True:
time.sleep(1)
traversed = set()
def depth_search(node, depth=0):
if depth == 4:
return
if child not in traversed:
depth_search(child, depth+1)

depth_search(nodes[random.randint(0, nc - 1)])

------

Diez

```