<｜begin▁of▁sentence｜>### Instruction:
In the realm of philanthropy, envision we're crafting a roadmap to streamline the flow of resources between benefactors and beneficiaries. Our landscape involves key philanthropic entities and initiatives, represented by a network where connections symbolize potential collaboration channels. The current layout of our philanthropic network includes key partnerships between entities 1 and 2 (the benefactors) and entities 3 and 4 (the beneficiaries), connected through collaborations as follows: [(1, 2), (1, 3), (2, 3), (2, 4), (3, 4)].

Our objective is to illuminate the most pivotal collaboration channels in guiding resources from benefactors to beneficiaries effectively. To achieve this, we're keen on assessing the importance of each partnership channel in facilitating resource flows, specifically considering paths initiating from our benefactors (entities 1 and 2) and culminating with our beneficiaries (entities 3 and 4). This involves a strategic analysis known as "betweenness centrality" for the collaboration channels, focusing solely on the routes from our chosen benefactors to the specified beneficiaries.

Could you assist in unfolding this strategic insight? This analysis is fundamental in refining our philanthropic endeavors, ensuring that resources are channelized through the most influential partnership pathways, amplifying our impact on the beneficiaries.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (2, 4), (3, 4)])

# Specify the subset of sources and targets
sources = [1, 2]
targets = [3, 4]

# Calculate edge betweenness centrality for the specified subsets
edge_betweenness = nx.edge_betweenness_centrality_subset(G, sources, targets, normalized=True, weight=None)

print(edge_betweenness)
<|EOT|>
