Went with BFS pretty quickly since the edges seemed unweighted at first glance.
Model the cities and edges as an unweighted graph and run BFS from the source to compute shortest distances to all reachable cities. Then sort the cities by distance ascending and, for ties, by city index ascending, returning the sorted list.
Pro tip: Clarify edge cases upfront: disconnected cities should be excluded, and if the graph is large, mention that BFS is O(V+E) while sorting adds O(V log V). Also, note that if the graph were weighted, Dijkstra's algorithm would be needed instead.
Confirm that edges are unweighted, the graph may be disconnected, and the output should include only reachable cities. Ask about input format and constraints.
Explain that BFS is optimal for unweighted graphs, giving shortest distances in O(V+E) time. Mention that Dijkstra's would be overkill here.
Use a queue to traverse from the source, track distances in an array or hash map, and mark visited cities to avoid cycles.
Collect all reachable cities with their distances, then sort by distance ascending and city index ascending for ties. Return the sorted list.
State time complexity O(V+E + V log V) and space O(V). Discuss disconnected components, source not in graph, and large inputs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.