<｜begin▁of▁sentence｜>### Instruction:
Imagine we're looking at the flow of data through a blockchain network, represented by a directed acyclic graph with transactions signified as edges: specifically, the transactions are (0 -> 1), (0 -> 3), and (1 -> 2). In such a data structure, akin to the branches of a tree, certain nodes can be considered "common ancestors" to various nodes where data converges.

In the context of analyzing transaction hierarchies within this blockchain model, could we leverage NetworkX to ascertain the "lowest common ancestor" within this network for all node pairings? Furthermore, for in-depth analysis, we're particularly interested in the transaction pathways related to the node pairs (1, 2) and (2, 3). Identifying these common ancestors can provide valuable insights when assessing the structure and flow of data within the blockchain, potentially revealing critical points within the network that warrant closer scrutiny.
### Response:
import networkx as nx

# Create a directed acyclic graph (DAG)
G = nx.DiGraph([(0, 1), (0, 3), (1, 2)])

# Compute the lowest common ancestor for all pairs
lca_dict = dict(nx.all_pairs_lowest_common_ancestor(G))
print(lca_dict)

# Compute the lowest common ancestor for specified pairs
specified_pairs = [(1, 2), (2, 3)]
lca_specified = dict(nx.all_pairs_lowest_common_ancestor(G, pairs=specified_pairs))
print(lca_specified)
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