/**

   @defgroup graph_adaptors Adaptor Classes for Graphs

   \brief This group contains several adaptor classes for graphs

   @ingroup 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

*/