I jumped straight to Dijkstra with a priority queue and that part went fine.
Start by clarifying the problem: this is a single-source shortest path problem on a directed graph with non-negative edge weights (latencies). Use Dijkstra's algorithm with a priority queue, then systematically address each edge case (disconnected graphs, zero-latency edges, cycles) and analyze time complexity.
Pro tip: Mention that zero-latency edges are fine for Dijkstra, but if negative latencies were possible, you'd need Bellman-Ford; also note that cycles don't break Dijkstra as long as weights are non-negative, but you should detect and handle them if they represent invalid configurations.
Confirm that latencies are non-negative, the graph is directed, and we need the minimum total latency from a single start to a single end. Ask if the graph is static or dynamic, and if there are any constraints on graph size.
Select Dijkstra's algorithm with a min-heap (priority queue) because it efficiently handles non-negative weights and finds the shortest path from one source to all nodes, from which we extract the target.
For disconnected graphs, return infinity or indicate no path. Zero-latency edges are allowed and do not affect correctness. Cycles are naturally handled by Dijkstra as long as weights are non-negative; if negative cycles exist, use Bellman-Ford and detect them.
With a binary heap, Dijkstra runs in O((V + E) log V) time and O(V) space. Mention that using a Fibonacci heap improves to O(E + V log V), but is rarely practical.
Compare with Bellman-Ford (O(VE)) for negative weights, BFS for unweighted graphs, and A* if a heuristic is available. Also mention that for very large graphs, bidirectional Dijkstra or contraction hierarchies can be faster.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.