Use one bounded CS branch where visible list work grows into one coherent graph-traversal bench, so sorting, binary search, adjacency, BFS, DFS, and visited-state behavior stay on compact live surfaces.
This topic stays deliberately bounded, but it now has a fuller process story. Sorting and Algorithmic Trade-offs keeps visible list work concrete first, Binary Search / Halving the Search Space reuses that same search language on ordered data, and the graph branch then moves into adjacency, breadth-first search, depth-first search, and frontier-versus-visited bookkeeping without pretending the site is becoming a full algorithms course.
Best first concepts
The topic page keeps these starts in their own compact row so the first screen is about orientation and next action, not stacked feature cards.
Watch sorting as visible work on a live list so input order, comparisons, and writes stay concrete instead of collapsing into one final answer.
Sorting as visible work
Strong first stop for getting into this topic without scanning the whole library.
Keep an ordered list, the low-mid-high markers, and the shrinking interval visible together so binary search feels visual instead of procedural.
Halving an ordered interval
Strong first stop for getting into this topic without scanning the whole library.
Keep one live graph, one local neighborhood, and one frontier cue visible together so graph structure feels readable before traversal rules get formal.
Graph neighborhoods and adjacency
Strong first stop for getting into this topic without scanning the whole library.
Keep the queue frontier, visited count, and graph layers visible together so breadth-first search reads as a layered process instead of a procedure list.
Layered frontier search
Strong first stop for getting into this topic without scanning the whole library.
Specific learning goals
These goal cards stay authored and transparent. They reuse the current topic page, starter tracks, guided collections, concept bundles, and progress cues instead of adding a separate recommendation system on top of this branch.
Use the algorithms-and-search topic page, the new playlist, the compact starter track, and the frontier-state revisit so the CS branch stays bounded while it grows into graph traversal.
Primary move
Open topic page
No saved progress yet inside Algorithms and Search.
Entry diagnostic
Start from the opening step
No saved diagnostic checks are available yet, so the opening step is still the best entry into the collection.
Reuses the guided collection entry for Algorithms and Search Playlist, with 0 of 2 probes already ready.
No saved progress yet inside Algorithms and Search.
Open the algorithms-and-search topic page is the next guided collection step.
Sorting and Algorithmic Trade-offs is the next best step inside Algorithms and Search Foundations.
No saved progress yet for Frontier and Visited State on Graphs.
Grouped concept overview
Each group is authored for this topic, but the actual concepts, progress badges, and track cues still come from the canonical concept metadata and shared progress model.
Group 01
Start with one live list where comparisons, writes, and remaining disorder stay visible together.
Group 02
Then reuse the same list language while low, mid, and high keep shrinking an ordered interval.
Group 03
Then carry the branch into one bounded graph bench where adjacency, queue-frontier BFS, stack-frontier DFS, and frontier-versus-visited bookkeeping can all be compared without leaving the same live graph.
Keep one live graph, one local neighborhood, and one frontier cue visible together so graph structure feels readable before traversal rules get formal.
Strong first stop for getting into this topic without scanning the whole library.
Keep the queue frontier, visited count, and graph layers visible together so breadth-first search reads as a layered process instead of a procedure list.
Strong first stop for getting into this topic without scanning the whole library.
Keep the stack frontier, current depth, and branch order visible together so depth-first search feels like disciplined backtracking instead of random wandering.
Use one cyclic graph to see which nodes are waiting, which are finished, and why repeat edges are skipped instead of reopening loops.
