<｜begin▁of▁sentence｜>### Instruction:
You're currently piecing together a complex network of relationships, which can be thought of as a map of varied interactions between individuals (or points of interest) labeled 1, 2, and 3. Within this intricate web, connections have different characteristics – some are friendly 'a', others are contentious 'b', and they each carry their own weight in terms of importance or influence, with numerical values like 5, 10, and 3.

As you sift through the information, you've come across a situation where it's crucial to focus only on the connections that have a significant impact. Specifically, you need to isolate and scrutinize the relationships that have a weight greater than 4 – as these are the ones that could yield vital clues or lead to breakthroughs in your investigation.

Your task is akin to applying a filter to this network's ledger, a record that might as well be contained within a 'MultiGraph'. On an abstract level, envision using a tool similar to the FilterMultiInner class from NetworkX to narrow down the list of interactions. This is not a request for the methodology but rather for a translation of this requirement into a relevant scenario where you, the detective, must extract only those pivotal connections with weight above 4 from the myriad of interactions recorded. How would you articulate this need within your investigative framework without delving into the specifics of the procedural aspects?
### Response:
import networkx as nx

# Creating a multigraph with some attributes
G = nx.MultiGraph()
G.add_edge(1, 2, key='a', weight=5)
G.add_edge(1, 2, key='b', weight=10)
G.add_edge(2, 3, key='a', weight=3)

# Filter the edges by weight greater than 4
edge_filter = lambda edge: G.get_edge_data(*edge)[edge[2]]['weight'] > 4
F = nx.classes.coreviews.FilterMultiInner(G.edges(keys=True), edge_filter)

# Iterate through the FilterMultiInner view and print the edges
print(list(F))
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