/**
* Saves a given graph to a dot file, it also creates the file, or overwrites the old one
*
* @param g : The jung graph to save
* @param filename : A string that points to the destination of the save
* @param labeler : A node object -> Node name converter object
* @param graphName : The name of the graph to export (usually this is set the project's name)
* @throws IOException On IO error
*/
public void save(Graph<V, E> g, String filename, Transformer<V, String> labeler, String graphName)
throws IOException {
SortedSet<V> nodes = new TreeSet<V>();
Map<V, SortedSet<V>> successors = new HashMap<V, SortedSet<V>>();
for (V source : g.getVertices()) {
Collection<V> actSuccessors = g.getSuccessors(source);
SortedSet<V> successorTree = new TreeSet<V>();
for (V destination : actSuccessors) {
successorTree.add(destination);
}
nodes.add(source);
successors.put(source, successorTree);
}
BufferedWriter writer =
new BufferedWriter(new OutputStreamWriter(new FileOutputStream(filename), "UTF-8"));
writer.write("digraph \"" + graphName + "\" {\n");
for (V from : nodes) {
Collection<V> actSuccessors = successors.get(from);
for (V to : actSuccessors) {
writer.write(
"\t\"" + labeler.transform(from) + "\" -> \"" + labeler.transform(to) + "\";\n");
}
if (g.getPredecessorCount(from) == 0 && actSuccessors.isEmpty()) {
writer.write("\t\"" + labeler.transform(from) + "\";\n");
}
}
writer.write("}");
writer.close();
}
@Override
public Box newVertexBox(Object vertex) {
for (Class c : types.keySet()) {
if (c.isInstance(vertex)) {
Transformer<Object, Box> t = types.get(c);
Box b = t.transform(vertex);
return b;
}
}
return newDefaultBox(vertex);
}
/**
* Calculates betweenness scores based on the all-pairs weighted shortest paths in the graph.
*
* <p>NOTE: This version of the algorithm may not work correctly on all graphs; we're still
* working out the bugs. Use at your own risk.
*
* @param graph the graph for which the scores are to be calculated
* @param edge_weights the edge weights to be used in the path length calculations
*/
public BetweennessCentrality(Graph<V, E> graph, Transformer<E, ? extends Number> edge_weights) {
// reject negative-weight edges up front
for (E e : graph.getEdges()) {
double e_weight = edge_weights.transform(e).doubleValue();
if (e_weight < 0)
throw new IllegalArgumentException("Weight for edge '" + e + "' is < 0: " + e_weight);
}
initialize(graph);
computeBetweenness(new MapBinaryHeap<V>(new BetweennessComparator()), edge_weights);
}
/**
* Returns the prior probability for <code>v</code>.
*
* @param v the vertex whose prior probability is being queried
* @return the prior probability for <code>v</code>
*/
protected S getVertexPrior(V v) {
return vertex_priors.transform(v);
}
protected void computeBetweenness(Queue<V> queue, Transformer<E, ? extends Number> edge_weights) {
for (V v : graph.getVertices()) {
// initialize the betweenness data for this new vertex
for (V s : graph.getVertices()) this.vertex_data.put(s, new BetweennessData());
// if (v.equals(new Integer(0)))
// System.out.println("pause");
vertex_data.get(v).numSPs = 1;
vertex_data.get(v).distance = 0;
Stack<V> stack = new Stack<V>();
// Buffer<V> queue = new UnboundedFifoBuffer<V>();
// queue.add(v);
queue.offer(v);
while (!queue.isEmpty()) {
// V w = queue.remove();
V w = queue.poll();
stack.push(w);
BetweennessData w_data = vertex_data.get(w);
for (E e : graph.getOutEdges(w)) {
// TODO (jrtom): change this to getOtherVertices(w, e)
V x = graph.getOpposite(w, e);
if (x.equals(w)) continue;
double wx_weight = edge_weights.transform(e).doubleValue();
// for(V x : graph.getSuccessors(w))
// {
// if (x.equals(w))
// continue;
// FIXME: the other problem is that I need to
// keep putting the neighbors of things we've just
// discovered in the queue, if they're undiscovered or
// at greater distance.
// FIXME: this is the problem, right here, I think:
// need to update position in queue if distance changes
// (which can only happen with weighted edges).
// for each outgoing edge e from w, get other end x
// if x not already visited (dist x < 0)
// set x's distance to w's dist + edge weight
// add x to queue; pri in queue is x's dist
// if w's dist + edge weight < x's dist
// update x's dist
// update x in queue (MapBinaryHeap)
// clear x's incoming edge list
// if w's dist + edge weight = x's dist
// add e to x's incoming edge list
BetweennessData x_data = vertex_data.get(x);
double x_potential_dist = w_data.distance + wx_weight;
if (x_data.distance < 0) {
// queue.add(x);
// vertex_data.get(x).distance =
// vertex_data.get(w).distance + 1;
x_data.distance = x_potential_dist;
queue.offer(x);
}
// note:
// (1) this can only happen with weighted edges
// (2) x's SP count and incoming edges are updated below
if (x_data.distance > x_potential_dist) {
x_data.distance = x_potential_dist;
// invalidate previously identified incoming edges
// (we have a new shortest path distance to x)
x_data.incomingEdges.clear();
// update x's position in queue
((MapBinaryHeap<V>) queue).update(x);
}
// if (vertex_data.get(x).distance ==
// vertex_data.get(w).distance + 1)
//
// if (x_data.distance == x_potential_dist)
// {
// x_data.numSPs += w_data.numSPs;
//// vertex_data.get(x).predecessors.add(w);
// x_data.incomingEdges.add(e);
// }
}
for (E e : graph.getOutEdges(w)) {
V x = graph.getOpposite(w, e);
if (x.equals(w)) continue;
double e_weight = edge_weights.transform(e).doubleValue();
BetweennessData x_data = vertex_data.get(x);
double x_potential_dist = w_data.distance + e_weight;
if (x_data.distance == x_potential_dist) {
x_data.numSPs += w_data.numSPs;
// vertex_data.get(x).predecessors.add(w);
x_data.incomingEdges.add(e);
}
}
}
while (!stack.isEmpty()) {
V x = stack.pop();
// for (V w : vertex_data.get(x).predecessors)
for (E e : vertex_data.get(x).incomingEdges) {
V w = graph.getOpposite(x, e);
double partialDependency =
vertex_data.get(w).numSPs
/ vertex_data.get(x).numSPs
* (1.0 + vertex_data.get(x).dependency);
vertex_data.get(w).dependency += partialDependency;
// E w_x = graph.findEdge(w, x);
// double w_x_score = edge_scores.get(w_x).doubleValue();
// w_x_score += partialDependency;
// edge_scores.put(w_x, w_x_score);
double e_score = edge_scores.get(e).doubleValue();
edge_scores.put(e, e_score + partialDependency);
}
if (!x.equals(v)) {
double x_score = vertex_scores.get(x).doubleValue();
x_score += vertex_data.get(x).dependency;
vertex_scores.put(x, x_score);
}
}
}
if (graph instanceof UndirectedGraph) {
for (V v : graph.getVertices()) {
double v_score = vertex_scores.get(v).doubleValue();
v_score /= 2.0;
vertex_scores.put(v, v_score);
}
for (E e : graph.getEdges()) {
double e_score = edge_scores.get(e).doubleValue();
e_score /= 2.0;
edge_scores.put(e, e_score);
}
}
vertex_data.clear();
}
