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Brandon Rozek 2023-02-18 13:12:02 -05:00
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@ -1,10 +1,12 @@
---
title: "Networkx Random Sample Graph"
date: 2022-04-07T19:48:12-04:00
date: 2022-04-07 19:48:12-04:00
draft: false
tags: ["Python"]
math: false
medium_enabled: true
medium_post_id: e5c5330cc9a7
tags:
- Python
title: Networkx Random Sample Graph
---
I've been working on several algorithms in `networkx`. In order to speed up testing, especially on large graphs, I've been randomly sampling portions of the original graph. The best way I've found to do this is through the following python snippet:
@ -24,4 +26,4 @@ random_nodes = random.sample(list(G.nodes), SAMPLE_SIZE)
G_sample = G.subgraph(random_nodes)
```
However, only considering the nodes when sampling makes it highly likely that the subgraph will significantly less edges. This results in a mostly disconnected subgraph and a loss of information. Sampling the edges prevents this issue at the expense of not capturing single nodes not connected to anything else.
However, only considering the nodes when sampling makes it highly likely that the subgraph will significantly less edges. This results in a mostly disconnected subgraph and a loss of information. Sampling the edges prevents this issue at the expense of not capturing single nodes not connected to anything else.