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In computer science, lexicographic breadth-first search or Lex-BFS is a linear time algorithm for ordering the vertices of a graph. The algorithm is different from a breadth-first search, but it produces an ordering that is consistent with breadth-first search.
The analysis highlights Applications, Standards and Science as prominent areas in the source structure around Lexicographic breadth-first search.
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Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
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The extracted context around Lexicographic breadth-first search shows recurring relationship patterns in the source. For example, Lexicographic breadth-first search → Continue, GFor, Therefore, Use Another extracted example is Lexicographic breadth-first search → Find, Initialize, Move. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
search graph breadth-first ordering algorithm vertices lexicographic sequence vertex set chordal output time graphs empty linear used coloring queue neighbors
TTTA extracted 15 structured relationships around Lexicographic breadth-first search. Examples in this analysis include breadth-first search → instance of → like simpler graph search algorithms and Lexicographic breadth-first search → has application → Bretscher. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| breadth-first search | instance of | like simpler graph search algorithms | 0.80 | text |
| depth-first search | instance of | like simpler graph search algorithms | 0.80 | text |
| this algorithm takes linear time.The algorithm is called lexicographic breadth-first search because the order it produces is an ordering that could also have been produced by a breadth-first search | instance of | like simpler graph search algorithms | 0.80 | text |
| and because if the ordering is used to index the rows | instance of | like simpler graph search algorithms | 0.80 | text |
| columns of an adjacency matrix of a graph then the algorithm sorts the rows | instance of | like simpler graph search algorithms | 0.80 | text |
| columns into lexicographical order | instance of | like simpler graph search algorithms | 0.80 | text |
| Lexicographic breadth-first search | has application | Bretscher | 0.60 | section |
| Lexicographic breadth-first search | has application | Habib | 0.60 | section |
| Lexicographic breadth-first search | related to Algorithm | Initialize | 0.60 | section |
| Lexicographic breadth-first search | related to Algorithm | Find | 0.60 | section |
| Lexicographic breadth-first search | related to Algorithm | Move | 0.60 | section |
| Lexicographic breadth-first search | related to Chordal graphs | Therefore | 0.60 | section |
The concept neighborhoods around Lexicographic breadth-first search bring nearby vocabulary together. In this analysis, examples include Search, Lexicographic and Ordering. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Lexicographic breadth-first search, one of the stronger structural bridges in this analysis connects Lexicographic breadth-first search with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Lexicographic breadth-first search to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications, Standards & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Lexicographic breadth-first search · EN edition · Analysis: TopicsToTalkAbout
