DSA · Theory

Searching in Graph Data Structure

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Theory

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Searching in Graph Data Structure

Don’t start Searching in Graph Data Structure with jargon. Start with bus routes. That is the whole point.

For Searching in Graph Data Structure, Big-O is in play. Don’t blame it until you have traced bus routes.

Without Searching in Graph Data Structure, a city map gets messy and bus routes is hard to trust.

Searching in Graph Data Structure shows up in a city map. Name bus routes, not “a real-world scenario”.

Don’t do this with Searching in Graph Data Structure: no visited set → infinite loop. Interviewers spot it in ten seconds.

After Searching in Graph Data Structure, Dev should still remember no visited set → infinite loop.

One breath for Searching in Graph Data Structure, then bus routes, then no visited set → infinite loop. Sit down.

Exam tip

Board: bus routes. Dry-run Searching in Graph Data Structure. Name the trap: no visited set → infinite loop.

Example

# adjacency list
graph = {
    "A": [("B", 2), ("C", 5)],
    "B": [("A", 2), ("C", 1)],
    "C": [("A", 5), ("B", 1)],
}
for u, edges in graph.items():
    print(u, "->", edges)

Searching in Graph Data Structure — graphs are often stored as adjacency lists: vertex → list of (neighbor, weight).

Short notes

DefSearching in Graph Data Structure — Dev uses it for bus routes in a city map.
RuleSearching in Graph Data Structure → BFS queue / DFS stack.
RememberSearching in Graph Data Structure + Big-O (a city map).
UseSearching in Graph Data Structure in a city map (bus routes).
TrapSearching in Graph Data Structure — no visited set → infinite loop.
ExSearching in Graph Data Structure → bus routes.

DSA · Theory

Searching in Graph Data Structure

← All stacks

Theory

810/810

Searching in Graph Data Structure

Don’t start Searching in Graph Data Structure with jargon. Start with bus routes. That is the whole point.

For Searching in Graph Data Structure, Big-O is in play. Don’t blame it until you have traced bus routes.

Without Searching in Graph Data Structure, a city map gets messy and bus routes is hard to trust.

Searching in Graph Data Structure shows up in a city map. Name bus routes, not “a real-world scenario”.

Don’t do this with Searching in Graph Data Structure: no visited set → infinite loop. Interviewers spot it in ten seconds.

After Searching in Graph Data Structure, Dev should still remember no visited set → infinite loop.

One breath for Searching in Graph Data Structure, then bus routes, then no visited set → infinite loop. Sit down.

Exam tip

Board: bus routes. Dry-run Searching in Graph Data Structure. Name the trap: no visited set → infinite loop.

Example

# adjacency list
graph = {
    "A": [("B", 2), ("C", 5)],
    "B": [("A", 2), ("C", 1)],
    "C": [("A", 5), ("B", 1)],
}
for u, edges in graph.items():
    print(u, "->", edges)

Searching in Graph Data Structure — graphs are often stored as adjacency lists: vertex → list of (neighbor, weight).

Short notes

DefSearching in Graph Data Structure — Dev uses it for bus routes in a city map.
RuleSearching in Graph Data Structure → BFS queue / DFS stack.
RememberSearching in Graph Data Structure + Big-O (a city map).
UseSearching in Graph Data Structure in a city map (bus routes).
TrapSearching in Graph Data Structure — no visited set → infinite loop.
ExSearching in Graph Data Structure → bus routes.

Searching in Graph Data Structure

Example

Short notes

Questions

Searching in Graph Data Structure

Example

Short notes

Questions