<｜begin▁of▁sentence｜>### Instruction:
Certainly. Imagine we're in a session, exploring the complex interconnected relationships and dynamics within a family system. We've decided to use a visual aid to better conceptualize these interactions, a sort of map detailing the family's relational network. In our therapeutic toolkit, we have a digital representation of this network, stored in a GraphML file named 'path_to_graph.graphml'. 

To effectively understand and navigate this graphical representation of family connections, we would benefit from transferring the data into a more manipulable form – specifically, into a NetworkX graph object. It is crucial for our analysis that each family member, represented as nodes, is identified by unique integers to preserve the clarity of connections. Similarly, the relationships, depicted as edges in our graph, should also be assigned integer identifiers to ensure seamless comprehension.

Once we've adeptly translated our GraphML file into this new format, maintaining the integrity of these integer identifications, I would like to review the connections – or the edges – to reflect upon the intricacies of the family's relational bonds. How might we go about this process while honouring the specifics of our data structure?
### Response:
import networkx as nx

# Read a graph from a GraphML file
G = nx.read_graphml('path_to_graph.graphml', node_type=int, edge_key_type=int)

# Print the edges of the graph
print(G.edges())
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