/**

    @defgroup graph_adaptors Adaptor Classes for Graphs

    @ingroup graphs

    \brief This group contains several adaptor classes for graphs

    The main parts of LEMON are the different graph structures, 

    generic graph algorithms, graph concepts which couple these, and 

    graph adaptors. While the previous notions are more or less clear, the 

    latter one needs further explanation. 

    Graph adaptors are graph classes which serve for considering graph 

    structures in different ways. 

    A short example makes this much 

    clearer. 

    Suppose that we have an instance \c g of a directed graph

    type say ListGraph and an algorithm 

    \code template<typename Graph> int algorithm(const Graph&); \endcode 

    is needed to run on the reversed oriented graph. 

    It may be expensive (in time or in memory usage) to copy 

    \c g with the reversed orientation. 

    In this case, an adaptor class is used, which 

    (according to LEMON graph concepts) works as a graph. 

    The adaptor uses 

    the original graph structure and graph operations when methods of the 

    reversed oriented graph are called. 

    This means that the adaptor have minor memory usage, 

    and do not perform sophisticated algorithmic actions. 

    The purpose of it is to give a tool for the cases when 

    a graph have to be used in a specific alteration. 

    If this alteration is obtained by a usual construction 

    like filtering the edge-set or considering a new orientation, then 

    an adaptor is worthwhile to use. 

    To come back to the reversed oriented graph, in this situation 

    \code template<typename Graph> class RevGraphAdaptor; \endcode 

    template class can be used. 

    The code looks as follows 

    \code

    ListGraph g;

    RevGraphAdaptor<ListGraph> rgw(g);

    int result=algorithm(rgw);

    \endcode

    After running the algorithm, the original graph \c g 

    is untouched. 

    This techniques gives rise to an elegant code, and 

    based on stable graph adaptors, complex algorithms can be 

    implemented easily. 

    In flow, circulation and bipartite matching problems, the residual 

    graph is of particular importance. Combining an adaptor implementing 

    this, shortest path algorithms and minimum mean cycle algorithms, 

    a range of weighted and cardinality optimization algorithms can be 

    obtained. 

    For other examples, 

    the interested user is referred to the detailed documentation of 

    particular adaptors. 

    The behavior of graph adaptors can be very different. Some of them keep 

    capabilities of the original graph while in other cases this would be 

    meaningless. This means that the concepts that they are models of depend 

    on the graph adaptor, and the wrapped graph(s). 

    If an edge of \c rgw is deleted, this is carried out by 

    deleting the corresponding edge of \c g, thus the adaptor modifies the 

    original graph. 

    But for a residual 

    graph, this operation has no sense. 

    Let us stand one more example here to simplify your work. 

    RevGraphAdaptor has constructor 

    \code 

    RevGraphAdaptor(Graph& _g);

    \endcode

    This means that in a situation, 

    when a <tt> const ListGraph& </tt> reference to a graph is given, 

    then it have to be instantiated with <tt>Graph=const ListGraph</tt>.

    \code

    int algorithm1(const ListGraph& g) {

    RevGraphAdaptor<const ListGraph> rgw(g);

    return algorithm2(rgw);

    }

    \endcode

 */