1 /* This file is part of libDAI - http://www.libdai.org/
2 *
4 * 2, or (at your option) any later version. libDAI is distributed without any
5 * warranty. See the file COPYING for more details.
6 *
7 * Copyright (C) 2006-2009 Joris Mooij [joris dot mooij at libdai dot org]
9 */
12 /// \file
13 /// \brief Defines the BipartiteGraph class, which represents a bipartite graph
16 #ifndef __defined_libdai_bipgraph_h
17 #define __defined_libdai_bipgraph_h
20 #include <ostream>
21 #include <vector>
22 #include <algorithm>
23 #include <dai/util.h>
24 #include <dai/exceptions.h>
27 namespace dai {
30 /// Represents the neighborhood structure of nodes in an undirected, bipartite graph.
31 /** A bipartite graph has two types of nodes: type 1 and type 2. Edges can occur only between
32 * nodes of different type. Nodes are indexed by an unsigned integer. If there are nr1()
33 * nodes of type 1 and nr2() nodes of type 2, the nodes of type 1 are numbered
34 * 0,1,2,...,nr1()-1 and the nodes of type 2 are numbered 0,1,2,...,nr2()-1. An edge
35 * between node \a n1 of type 1 and node \a n2 of type 2 is represented by a BipartiteGraph::Edge(\a n1,\a n2).
36 *
37 * A BipartiteGraph is implemented as a sparse adjacency list, i.e., it stores for each node a list of
38 * its neighboring nodes. More precisely: it stores for each node of type 1 a vector of Neighbor structures
39 * (accessible by the nb1() method) describing the neighboring nodes of type 2; similarly, for each node
40 * of type 2 it stores a vector of Neighbor structures (accessibly by the nb2() method) describing the
41 * neighboring nodes of type 1.
42 * Thus, each node has an associated variable of type BipartiteGraph::Neighbors, which is a vector of
43 * Neighbor structures, describing its neighboring nodes of the other type.
44 * \idea Cache second-order neighborhoods in BipartiteGraph.
45 */
46 class BipartiteGraph {
47 public:
48 /// Describes the neighbor relationship of two nodes in a BipartiteGraph.
49 /** Sometimes we want to do an action, such as sending a
50 * message, for all edges in a graph. However, most graphs
51 * will be sparse, so we need some way of storing a set of
52 * the neighbors of a node, which is both fast and
53 * memory-efficient. We also need to be able to go between
54 * viewing node \a a as a neighbor of node \a b, and node \a b
55 * as a neighbor of node \a a. The Neighbor struct solves
56 * both of these problems. Each node has a list of neighbors,
58 * extra information is included in the Neighbor struct which
59 * allows us to access a node as a neighbor of its neighbor
60 * (the \c dual member).
61 *
62 * By convention, variable identifiers naming indices into a
63 * vector of neighbors are prefixed with an underscore ("_").
64 * The neighbor list which they point into is then understood
65 * from context. For example:
66 *
67 * \code
68 * void BP::calcNewMessage( size_t i, size_t _I )
69 * \endcode
70 *
71 * Here, \a i is the "absolute" index of node i, but \a _I is
72 * understood as a "relative" index, giving node \a I 's entry in
73 * <tt>nb1(i)</tt>. The corresponding Neighbor structure can be
74 * accessed as <tt>nb1(i,_I)</tt> or <tt>nb1(i)[_I]</tt>. The
75 * absolute index of \a _I, which would be called \a I, can be
76 * recovered from the \c node member: <tt>nb1(i,_I).node</tt>.
77 * The \c iter member gives the relative index \a _I, and the
78 * \c dual member gives the "dual" relative index, i.e., the
79 * index of \a i in \a I 's neighbor list.
80 *
81 * \code
82 * Neighbor n = nb1(i,_I);
83 * n.node == I &&
84 * n.iter == _I &&
85 * nb2(n.node,n.dual).node == i
86 * \endcode
87 *
88 * In a FactorGraph, the nodes of type 1 represent variables, and
89 * the nodes of type 2 represent factors. For convenience, nb1() is
90 * called FactorGraph::nbV(), and nb2() is called FactorGraph::nbF().
91 *
92 * There is no easy way to transform a pair of absolute node
93 * indices \a i and \a I into a Neighbor structure relative
94 * to one of the nodes. Such a feature has never yet been
95 * found to be necessary. Iteration over edges can always be
96 * accomplished using the Neighbor lists, and by writing
97 * functions that accept relative indices:
98 * \code
99 * for( size_t i = 0; i < nrVars(); ++i )
100 * foreach( const Neighbor &I, nbV(i) )
101 * calcNewMessage( i, I.iter );
102 * \endcode
103 */
104 struct Neighbor {
105 /// Corresponds to the index of this Neighbor entry in the vector of neighbors
106 size_t iter;
107 /// Contains the number of the neighboring node
108 size_t node;
109 /// Contains the "dual" iter
110 size_t dual;
112 /// Default constructor
113 Neighbor() {}
114 /// Constructor that sets the Neighbor members according to the parameters
115 Neighbor( size_t iter, size_t node, size_t dual ) : iter(iter), node(node), dual(dual) {}
117 /// Cast to \c size_t returns \c node member
118 operator size_t () const { return node; }
119 };
121 /// Describes the neighbors of some node.
122 typedef std::vector<Neighbor> Neighbors;
124 /// Represents an edge: an Edge(\a n1,\a n2) corresponds to the edge between node \a n1 of type 1 and node \a n2 of type 2.
125 typedef std::pair<size_t,size_t> Edge;
127 private:
128 /// Contains for each node of type 1 a vector of its neighbors
129 std::vector<Neighbors> _nb1;
131 /// Contains for each node of type 2 a vector of its neighbors
132 std::vector<Neighbors> _nb2;
134 /// Used internally by isTree()
135 struct levelType {
136 /// Indices of nodes of type 1
137 std::vector<size_t> ind1;
138 /// Indices of nodes of type 2
139 std::vector<size_t> ind2;
140 };
142 // OBSOLETE
143 /// \name Backwards compatibility layer (to be removed soon)
144 //@{
145 /// Enable backwards compatibility layer?
147 */
148 bool _edge_indexed;
149 /// Call indexEdges() first to initialize these members
151 */
152 std::vector<Edge> _edges;
153 /// Call indexEdges() first to initialize these members
155 */
156 hash_map<Edge,size_t> _vv2e;
157 //@}
159 public:
160 /// \name Constructors and destructors
161 //@{
162 /// Default constructor (creates an empty bipartite graph)
163 BipartiteGraph() : _nb1(), _nb2(), _edge_indexed(false) {}
165 /// Constructs BipartiteGraph from a range of edges.
166 /** \tparam EdgeInputIterator Iterator that iterates over instances of BipartiteGraph::Edge.
167 * \param nr1 The number of nodes of type 1.
168 * \param nr2 The number of nodes of type 2.
169 * \param begin Points to the first edge.
170 * \param end Points just beyond the last edge.
171 */
172 template<typename EdgeInputIterator>
173 BipartiteGraph( size_t nr1, size_t nr2, EdgeInputIterator begin, EdgeInputIterator end ) : _nb1( nr1 ), _nb2( nr2 ), _edge_indexed(false) {
174 construct( nr1, nr2, begin, end );
175 }
176 //@}
178 /// \name Accessors and mutators
179 //@{
180 /// Returns constant reference to the \a _i2 'th neighbor of node \a i1 of type 1
181 const Neighbor & nb1( size_t i1, size_t _i2 ) const {
182 DAI_DEBASSERT( i1 < _nb1.size() );
183 DAI_DEBASSERT( _i2 < _nb1[i1].size() );
184 return _nb1[i1][_i2];
185 }
186 /// Returns reference to the \a _i2 'th neighbor of node \a i1 of type 1
187 Neighbor & nb1( size_t i1, size_t _i2 ) {
188 DAI_DEBASSERT( i1 < _nb1.size() );
189 DAI_DEBASSERT( _i2 < _nb1[i1].size() );
190 return _nb1[i1][_i2];
191 }
193 /// Returns constant reference to the \a _i1 'th neighbor of node \a i2 of type 2
194 const Neighbor & nb2( size_t i2, size_t _i1 ) const {
195 DAI_DEBASSERT( i2 < _nb2.size() );
196 DAI_DEBASSERT( _i1 < _nb2[i2].size() );
197 return _nb2[i2][_i1];
198 }
199 /// Returns reference to the \a _i1 'th neighbor of node \a i2 of type 2
200 Neighbor & nb2( size_t i2, size_t _i1 ) {
201 DAI_DEBASSERT( i2 < _nb2.size() );
202 DAI_DEBASSERT( _i1 < _nb2[i2].size() );
203 return _nb2[i2][_i1];
204 }
206 /// Returns constant reference to all neighbors of node \a i1 of type 1
207 const Neighbors & nb1( size_t i1 ) const {
208 DAI_DEBASSERT( i1 < _nb1.size() );
209 return _nb1[i1];
210 }
211 /// Returns reference to all neighbors of node \a i1 of type 1
212 Neighbors & nb1( size_t i1 ) {
213 DAI_DEBASSERT( i1 < _nb1.size() );
214 return _nb1[i1];
215 }
217 /// Returns constant reference to all neighbors of node \a i2 of type 2
218 const Neighbors & nb2( size_t i2 ) const {
219 DAI_DEBASSERT( i2 < _nb2.size() );
220 return _nb2[i2];
221 }
222 /// Returns reference to all neighbors of node \a i2 of type 2
223 Neighbors & nb2( size_t i2 ) {
224 DAI_DEBASSERT( i2 < _nb2.size() );
225 return _nb2[i2];
226 }
227 //@}
229 /// \name Adding nodes and edges
230 //@{
231 /// (Re)constructs BipartiteGraph from a range of edges.
232 /** \tparam EdgeInputIterator Iterator that iterates over instances of BipartiteGraph::Edge.
233 * \param nr1 The number of nodes of type 1.
234 * \param nr2 The number of nodes of type 2.
235 * \param begin Points to the first edge.
236 * \param end Points just beyond the last edge.
237 */
238 template<typename EdgeInputIterator>
239 void construct( size_t nr1, size_t nr2, EdgeInputIterator begin, EdgeInputIterator end );
241 /// Adds a node of type 1 without neighbors and returns the index of the added node.
242 size_t add1() { _nb1.push_back( Neighbors() ); return _nb1.size() - 1; }
244 /// Adds a node of type 2 without neighbors and returns the index of the added node.
245 size_t add2() { _nb2.push_back( Neighbors() ); return _nb2.size() - 1; }
247 /// Adds a node of type 1, with neighbors specified by a range of nodes of type 2.
248 /** \tparam NodeInputIterator Iterator that iterates over instances of \c size_t.
249 * \param begin Points to the first index of the nodes of type 2 that should become neighbors of the added node.
250 * \param end Points just beyond the last index of the nodes of type 2 that should become neighbors of the added node.
251 * \param sizeHint For improved efficiency, the size of the range may be specified by \a sizeHint.
252 * \returns Index of the added node.
253 */
254 template <typename NodeInputIterator>
255 size_t add1( NodeInputIterator begin, NodeInputIterator end, size_t sizeHint = 0 ) {
256 Neighbors nbs1new;
257 nbs1new.reserve( sizeHint );
258 size_t iter = 0;
259 for( NodeInputIterator it = begin; it != end; ++it ) {
260 DAI_ASSERT( *it < nr2() );
261 Neighbor nb1new( iter, *it, nb2(*it).size() );
262 Neighbor nb2new( nb2(*it).size(), nr1(), iter++ );
263 nbs1new.push_back( nb1new );
264 nb2( *it ).push_back( nb2new );
265 }
266 _nb1.push_back( nbs1new );
267 return _nb1.size() - 1;
268 }
270 /// Adds a node of type 2, with neighbors specified by a range of nodes of type 1.
271 /** \tparam NodeInputIterator Iterator that iterates over instances of \c size_t.
272 * \param begin Points to the first index of the nodes of type 1 that should become neighbors of the added node.
273 * \param end Points just beyond the last index of the nodes of type 1 that should become neighbors of the added node.
274 * \param sizeHint For improved efficiency, the size of the range may be specified by \a sizeHint.
275 * \returns Index of the added node.
276 */
277 template <typename NodeInputIterator>
278 size_t add2( NodeInputIterator begin, NodeInputIterator end, size_t sizeHint = 0 ) {
279 Neighbors nbs2new;
280 nbs2new.reserve( sizeHint );
281 size_t iter = 0;
282 for( NodeInputIterator it = begin; it != end; ++it ) {
283 DAI_ASSERT( *it < nr1() );
284 Neighbor nb2new( iter, *it, nb1(*it).size() );
285 Neighbor nb1new( nb1(*it).size(), nr2(), iter++ );
286 nbs2new.push_back( nb2new );
287 nb1( *it ).push_back( nb1new );
288 }
289 _nb2.push_back( nbs2new );
290 return _nb2.size() - 1;
291 }
293 /// Adds an edge between node \a n1 of type 1 and node \a n2 of type 2.
294 /** If \a check == \c true, only adds the edge if it does not exist already.
295 */
296 void addEdge( size_t n1, size_t n2, bool check = true );
297 //@}
299 /// \name Erasing nodes and edges
300 //@{
301 /// Removes node \a n1 of type 1 and all incident edges; indices of other nodes are changed accordingly.
302 void erase1( size_t n1 );
304 /// Removes node \a n2 of type 2 and all incident edges; indices of other nodes are changed accordingly.
305 void erase2( size_t n2 );
307 /// Removes edge between node \a n1 of type 1 and node \a n2 of type 2.
308 void eraseEdge( size_t n1, size_t n2 );
309 //@}
311 /// \name Queries
312 //@{
313 /// Returns number of nodes of type 1
314 size_t nr1() const { return _nb1.size(); }
315 /// Returns number of nodes of type 2
316 size_t nr2() const { return _nb2.size(); }
318 /// Calculates the number of edges, time complexity: O(nr1())
319 size_t nrEdges() const {
320 size_t sum = 0;
321 for( size_t i1 = 0; i1 < nr1(); i1++ )
322 sum += nb1(i1).size();
323 return sum;
324 }
326 /// Calculates second-order neighbors (i.e., neighbors of neighbors) of node \a n1 of type 1.
327 /** If \a include == \c true, includes \a n1 itself, otherwise excludes \a n1.
328 */
329 std::vector<size_t> delta1( size_t n1, bool include = false ) const;
331 /// Calculates second-order neighbors (i.e., neighbors of neighbors) of node \a n2 of type 2.
332 /** If \a include == \c true, includes \a n2 itself, otherwise excludes \a n2.
333 */
334 std::vector<size_t> delta2( size_t n2, bool include = false ) const;
336 /// Returns true if the graph is connected
337 /** \todo Should be optimized by invoking boost::graph library
338 */
339 bool isConnected() const;
341 /// Returns true if the graph is a tree, i.e., if it is singly connected and connected.
342 bool isTree() const;
344 /// Checks internal consistency
345 void checkConsistency() const;
346 //@}
348 /// \name Input and output
349 //@{
350 /// Writes this BipartiteGraph to an output stream in GraphViz .dot syntax
351 void printDot( std::ostream& os ) const;
352 //@}
354 // OBSOLETE
355 /// \name Backwards compatibility layer (to be removed soon)
356 //@{
357 /// Prepare backwards compatibility layer for indexed edges
359 */
360 void indexEdges() {
361 std::cerr << "Warning: this BipartiteGraph edge interface is obsolete!" << std::endl;
362 _edges.clear();
363 _vv2e.clear();
364 size_t i=0;
365 foreach(const Neighbors &nb1s, _nb1) {
366 foreach(const Neighbor &n2, nb1s) {
367 Edge e(i, n2.node);
368 _edges.push_back(e);
369 }
370 i++;
371 }
372 sort(_edges.begin(), _edges.end()); // unnecessary?
374 i=0;
375 foreach(const Edge& e, _edges) {
376 _vv2e[e] = i++;
377 }
379 _edge_indexed = true;
380 }
382 /// Returns edge with index \a e
384 */
385 const Edge& edge(size_t e) const {
386 DAI_ASSERT(_edge_indexed);
387 return _edges[e];
388 }
390 /// Returns all edges
392 */
393 const std::vector<Edge>& edges() const {
394 return _edges;
395 }
397 /// Converts a pair of node indices to an edge index
399 */
400 size_t VV2E(size_t n1, size_t n2) const {
401 DAI_ASSERT(_edge_indexed);
402 Edge e(n1,n2);
403 hash_map<Edge,size_t>::const_iterator i = _vv2e.find(e);
404 DAI_ASSERT(i != _vv2e.end());
405 return i->second;
406 }
408 /// Returns number of edges
410 */
411 size_t nr_edges() const {
412 DAI_ASSERT(_edge_indexed);
413 return _edges.size();
414 }
415 //@}
416 };
419 template<typename EdgeInputIterator>
420 void BipartiteGraph::construct( size_t nr1, size_t nr2, EdgeInputIterator begin, EdgeInputIterator end ) {
421 _nb1.clear();
422 _nb1.resize( nr1 );
423 _nb2.clear();
424 _nb2.resize( nr2 );
426 for( EdgeInputIterator e = begin; e != end; e++ ) {
427 #ifdef DAI_DEBUG
428 addEdge( e->first, e->second, true );
429 #else
430 addEdge( e->first, e->second, false );
431 #endif
432 }
433 }
436 } // end of namespace dai
439 /** \example example_bipgraph.cpp
440 * This example deals with the following bipartite graph:
441 * \dot
442 * graph example {
443 * ordering=out;
444 * subgraph cluster_type1 {
445 * node[shape=circle,width=0.4,fixedsize=true,style=filled];
446 * 12 [label="2"];
447 * 11 [label="1"];
448 * 10 [label="0"];
449 * }
450 * subgraph cluster_type2 {
451 * node[shape=polygon,regular=true,sides=4,width=0.4,fixedsize=true,style=filled];
452 * 21 [label="1"];
453 * 20 [label="0"];
454 * }
455 * 10 -- 20;
456 * 11 -- 20;
457 * 12 -- 20;
458 * 11 -- 21;
459 * 12 -- 21;
460 * }
461 * \enddot
462 * It has three nodes of type 1 (drawn as circles) and two nodes of type 2 (drawn as rectangles).
463 * Node 0 of type 1 has only one neighbor (node 0 of type 2), but node 0 of type 2 has three neighbors (nodes 0,1,2 of type 1).
464 * The example code shows how to construct a BipartiteGraph object representing this bipartite graph and
465 * how to iterate over nodes and their neighbors.
466 *
467 * \section Output
468 * \verbinclude examples/example_bipgraph.out
469 *
470 * \section Source
471 */
474 #endif