[Python] How to efficiently apply batched edge/node modifications across multiple graph copies...

I'm working on a task where I need to apply custom modifications to many independent copies of the same base graph. For each instance, I want to:

• Start from the same base graph
• Remove one or more edges
• Insert intermediate nodes
• Add new edges with custom weights

I already have all the necessary modification data (bulk of edges to remove, nodes to add, and edges to insert) prepared up front as NumPy arrays or lists — one set per graph copy.

What I’m doing now (with networkx):

import networkx as nx

# Base graph
G = nx.Graph()
G.add_edge(0, 1, DISTANCE=10)
G.add_edge(1, 2, DISTANCE=10)

# Example modification for one instance
G_copy = G.copy()
G_copy.remove_edge(0, 1)
G_copy.add_node(100)
G_copy.add_edge(0, 100, DISTANCE=4)
G_copy.add_edge(100, 1, DISTANCE=6)


I need to apply different sets of modifications (edge removals, node insertions, edge additions with weights) to thousands of independent copies of a base graph. All modification data is precomputed in bulk. The bottleneck is the sequential processing: each copy is handled one at a time in Python using loops. Even with all data available upfront, networkx lacks support for efficient batched or parallel updates.

I'm looking for a graph library that supports fast, batched modifications across many graph instances, ideally with:

• NumPy-like vectorized graph operations
• Efficient edge/node insertions and deletions

To be concise:

I’m looking for a way to apply the following batched operations on multiple independent graph copies, using precomputed data:

G.remove_edges(first_nodes, last_nodes)
G.add_nodes(middle_nodes)
G.add_edges(first_nodes, middle_nodes, DISTANCE=first_middle_distance)
G.add_edges(middle_nodes, last_nodes, DISTANCE=middle_last_distance)


Here, first_nodes, last_nodes, middle_nodes, first_middle_distance, and middle_last_distance are all precomputed. The goal is to perform these modifications efficiently across many G copies in batch — ideally returning one modified graph per index in the input arrays.

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