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  2. Adjacency list - Wikipedia

    en.wikipedia.org/wiki/Adjacency_list

    An adjacency list representation for a graph associates each vertex in the graph with the collection of its neighbouring vertices or edges. There are many variations of this basic idea, differing in the details of how they implement the association between vertices and collections, in how they implement the collections, in whether they include both vertices and edges or only vertices as first ...

  3. Implicit graph - Wikipedia

    en.wikipedia.org/wiki/Implicit_graph

    In the context of efficient representations of graphs, J. H. Muller defined a local structure or adjacency labeling scheme for a graph G in a given family F of graphs to be an assignment of an O(log n)-bit identifier to each vertex of G, together with an algorithm (that may depend on F but is independent of the individual graph G) that takes as input two vertex identifiers and determines ...

  4. Depth-first search - Wikipedia

    en.wikipedia.org/wiki/Depth-first_search

    A decision version of the problem (testing whether some vertex u occurs before some vertex v in this order) is P-complete, [12] meaning that it is "a nightmare for parallel processing". [13]: 189 A depth-first search ordering (not necessarily the lexicographic one), can be computed by a randomized parallel algorithm in the complexity class RNC ...

  5. Kosaraju's algorithm - Wikipedia

    en.wikipedia.org/wiki/Kosaraju's_algorithm

    Provided the graph is described using an adjacency list, Kosaraju's algorithm performs two complete traversals of the graph and so runs in Θ(V+E) (linear) time, which is asymptotically optimal because there is a matching lower bound (any algorithm must examine all vertices and edges).

  6. Tarjan's strongly connected components algorithm - Wikipedia

    en.wikipedia.org/wiki/Tarjan's_strongly_connected...

    The basic idea of the algorithm is this: a depth-first search (DFS) begins from an arbitrary start node (and subsequent depth-first searches are conducted on any nodes that have not yet been found). As usual with depth-first search, the search visits every node of the graph exactly once, refusing to revisit any node that has already been visited.

  7. External memory graph traversal - Wikipedia

    en.wikipedia.org/wiki/External_memory_graph...

    The adjacency lists for the remaining nodes that could not be found in H need to be fetched. A scan over L ( t − 1 ) {\displaystyle L(t-1)} yields the partition identifiers. After sorting and deletion of duplicates, the respective files F i {\displaystyle F_{i}} can be concatenated into a temporary file F' .

  8. An analysis of 2024 Google search data revealed the top health questions asked by Americans. A registered nurse provides answers to the seven most common inquiries.

  9. Parallel breadth-first search - Wikipedia

    en.wikipedia.org/wiki/Parallel_breadth-first_search

    In the example on the left, there are two arrays, C and R. Array C stores the adjacency lists of all nodes. Array R stored the index in C, the entry R[i] points to the beginning index of adjacency lists of vertex i in array C. The CSR is extremely fast because it costs only constant time to access vertex adjacency.