Basic Graph Algorithms ( basic_graph_alg )

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bool	TOPSORT(graph G, node_array<int>& ord)
		TOPSORT takes as argument a directed graph G(V, E). It sorts G topologically (if G is acyclic) by computing for every node v $\in$ V an integer ord[v] such that 1 < = ord[v] < = | V| and ord[v] < ord[w] for all edges (v, w) $\in$ E. TOPSORT returns true if G is acyclic and false otherwise. The algorithm ([44]) has running time O(| V| + | E|).
list<node>	DFS(graph G, node s, node_array<bool>& reached)
		DFS takes as argument a directed graph G(V, E), a node s of G and a node_array reached of boolean values. It performs a depth first search starting at s visiting all reachable nodes v with reached[v] = false. For every visited node v reached[v] is changed to true. DFS returns the list of all reached nodes. The algorithm ([76]) has running time O(| V| + | E|).
list<edge>	DFS_NUM(graph G, node_array<int>& dfsnum, node_array<int>& compnum)
		DFS_NUM takes as argument a directed graph G(V, E). It performs a depth first search of G numbering the nodes of G in two different ways. dfsnum is a numbering with respect to the calling time and compnum a numbering with respect to the completion time of the recursive calls. DFS_NUM returns a depth first search forest of G (list of tree edges). The algorithm ([76]) has running time O(| V| + | E|).
list<node>	BFS(graph G, node s, node_array<int>& dist)
		BFS takes as argument a directed graph G(V, E),a node s of G and a node array dist of integers. It performs a breadth first search starting at s visiting all nodes v with dist[v] = - 1 reachable from s. The dist value of every visited node is replaced by its distance to s. BFS returns the list of all visited nodes. The algorithm ([52]) has running time O(| V| + | E|).
list<node>	BFS(graph G, node s, node_array<int>& dist, node_array<edge>& pred)
		performs a bread first search as described above and computes for every node v the predecessor edge pred[v] in the bfs shortest path tree.
int	COMPONENTS(graph G, node_array<int>& compnum)
		COMPONENTS takes a graph G(V, E) as argument and computes the connected components of the underlying undirected graph, i.e., for every node v $\in$ V an integer compnum[v] from [0...c - 1] where c is the number of connected components of G and v belongs to the i-th connected component iff compnum[v] = i. COMPONENTS returns c. The algorithm ([52]) has running time O(| V| + | E|).
int	STRONG_COMPONENTS(graph G, node_array<int>& compnum)
		STRONG_COMPONENTS takes a directed graph G(V, E) as argument and computes for every node v $\in$ V an integer compnum[v] from [0...c - 1] where c is the number of strongly connected components of G and v belongs to the i-th strongly connected component iff compnum[v] = i. STRONG_COMPONENTS returns c. The algorithm ([52]) has running time O(| V| + | E|).
int	BICONNECTED_COMPONENTS(graph G, edge_array<int>& compnum)
		BICONNECTED_COMPONENTS computes the biconnected components of the undirected version of G. A biconnected component of an undirected graph is a maximal biconnected subgraph and a biconnected graph is a graph which cannot be disconnected by removing one of its nodes. A graph having only one node is biconnected. Let c be the number of biconnected component and let c' be the number of biconnected components containing at least one edge, c - c' is the number of isolated nodes in G, where a node v is isolated if is not connected to a node different from v (it may be incident to self-loops). The function returns c and labels each edge of G (which is not a self-loop) by an integer in [0...c' - 1]. Two edges receive the same label iff they belong to the same biconnected component. The edge labels are returned in compnum. Be aware that self-loops receive no label since self-loops are ignored when interpreting a graph as an undirected graph. The algorithm ([18]) has running time O(| V| + | E|).
graph	TRANSITIVE_CLOSURE(graph G)
		TRANSITIVE_CLOSURE takes a directed graph G(V, E) as argument and computes the transitive closure of G(V, E). It returns a directed graph G'(V', E') with V' = V and (v, w) $\in$ E' $\Leftrightarrow$ there is a path from v to w in G. The algorithm ([36]) has running time O(| V|*| E|).