property_matrix<ActorSharedPtr,LayerSharedPtr,bool> actor_existence_property_matrix(const MLNetworkSharedPtr& mnet) {
property_matrix<ActorSharedPtr,LayerSharedPtr,bool> P(mnet->get_actors()->size(),mnet->get_layers()->size(),false);
for (LayerSharedPtr layer: *mnet->get_layers())
for (NodeSharedPtr node: *mnet->get_nodes(layer)) {
P.set(node->actor,layer,true);
}
return P;
}
std::string tau_transition::fire(MLNetworkSharedPtr& mnet, const NodeSharedPtr& node, long time) {
std::string current_status = mnet->actor_features()->getString(node->actor->id,_S_current);
if (current_status==status) {
if (time - mnet->actor_features()->getNumeric(node->actor->id,_S_time)>=tau) {
return new_status;
}
}
return "";
}
std::string beta_transition::fire(MLNetworkSharedPtr& mnet, const NodeSharedPtr& node, long time) {
std::string current_status = mnet->actor_features()->getString(node->actor->id,_S_current);
if (current_status==status) {
for (NodeSharedPtr n: mnet->neighbors(node,IN)) {
if (mnet->actor_features()->getString(n->actor->id,_S_current)==neighbor_status && test(beta)) {
return new_status;
}
}
}
return "";
}
property_matrix<ActorSharedPtr,LayerSharedPtr,double> actor_cc_property_matrix(const MLNetworkSharedPtr& mnet) {
property_matrix<ActorSharedPtr,LayerSharedPtr,double> P(mnet->get_actors()->size(),mnet->get_layers()->size(),0);
for (ActorSharedPtr actor: *mnet->get_actors()) {
for (LayerSharedPtr layer: *mnet->get_layers()) {
NodeSharedPtr node = mnet->get_node(actor,layer);
if (!node) P.set_na(actor,layer);
else P.set(actor,layer,cc(mnet,node));
}
}
return P;
}
// TODO document: does not consider self edges
property_matrix<dyad,LayerSharedPtr,bool> edge_existence_property_matrix(const MLNetworkSharedPtr& mnet) {
long n = mnet->get_actors()->size();
property_matrix<dyad,LayerSharedPtr,bool> P(n*(n-1)/2,mnet->get_layers()->size(),false);
for (LayerSharedPtr layer: *mnet->get_layers()) {
for (EdgeSharedPtr e: *mnet->get_edges(layer,layer)) {
dyad d(e->v1->actor,e->v2->actor);
P.set(d,layer,true);
}
}
return P;
}
void test_community_single_layer() {
test_begin("Label propagation, single layer");
MLNetworkSharedPtr mnet = read_multilayer("toy.mpx","cpm",',');
CommunityStructureSharedPtr c = label_propagation_single(mnet, mnet->get_layer("Work"));
std::cout << c->to_string() << std::endl;
test_end("Label propagation, single layer");
}
double relevance(const MLNetworkSharedPtr& mnet, const ActorSharedPtr& actor, const LayerSharedPtr& layer, edge_mode mode) {
set<actor_id> neighbors_on_selected_layers;
set<actor_id> all_neighbors;
for (NodeSharedPtr node: *mnet->get_nodes(actor)) {
for (NodeSharedPtr neighbor: *mnet->neighbors(node, mode)) {
all_neighbors.insert(neighbor->actor->id);
if (layer == neighbor->layer)
neighbors_on_selected_layers.insert(neighbor->actor->id);
}
}
if (all_neighbors.size()==0) return 0; // by definition
else return (double)neighbors_on_selected_layers.size()/all_neighbors.size();
}
double cc(const MLNetworkSharedPtr& mnet, const NodeSharedPtr& node) {
int num_edges = 0;
NodeListSharedPtr neigh = mnet->neighbors(node,INOUT);
int num_neigh = neigh->size();
if (num_neigh<=1) return 0.0;
for (NodeSharedPtr n1: *neigh) {
for (NodeSharedPtr n2: *neigh) {
if (n1>=n2) continue;
if (mnet->get_edge(n1,n2))
num_edges++;
}
}
return num_edges*2.0/(num_neigh*(num_neigh-1));
}
double xrelevance(const MLNetworkSharedPtr& mnet, const ActorSharedPtr& actor, const std::unordered_set<LayerSharedPtr>& layers, edge_mode mode) {
set<actor_id> neighbors_on_selected_layers;
set<actor_id> neighbors_on_other_layers;
set<actor_id> all_neighbors;
for (NodeSharedPtr node: *mnet->get_nodes(actor)) {
for (NodeSharedPtr neighbor: *mnet->neighbors(node, mode)) {
all_neighbors.insert(neighbor->actor->id);
if (layers.count(neighbor->layer)>0)
neighbors_on_selected_layers.insert(neighbor->actor->id);
else neighbors_on_other_layers.insert(neighbor->actor->id);
}
}
if (all_neighbors.size()==0) return 0; // by definition
else {
for (actor_id actor: neighbors_on_other_layers)
neighbors_on_selected_layers.erase(actor);
return (double)neighbors_on_selected_layers.size()/all_neighbors.size();
}
}
void test_double_layer() {
{
log(1);
// create multiplex network
MLNetworkSharedPtr mnet = read_multilayer("test/data/ACModel_BA_copy.csv","mlnet 2",',');
// BEGIN Getting Layers
layer_list layers_l = mnet->get_layers();
std::unordered_set<LayerSharedPtr> layers;
for(LayerSharedPtr layer : layers_l){
layers.insert(layer);
}
// END Getting Layers
/*
std::cout<<"[.] NODE Jaccard :"<<std::endl;
for(LayerSharedPtr layer1 : layers){
for(LayerSharedPtr layer2 : layers){
double jaccard_sim = jaccard_node_similarity(mnet, layer1, layer2);
std::cout<<jaccard_sim<<" - ";
}
std::cout<<std::endl;
}
std::cout<<"[.] EDGE Jaccard :"<<std::endl;
for(LayerSharedPtr layer1 : layers){
for(LayerSharedPtr layer2 : layers){
double jaccard_edge_sim = jaccard_similarity(mnet, layer1, layer2);
std::cout<<jaccard_edge_sim<<" - ";
}
std::cout<<std::endl;
}*/
benchmark_adjust_error(mnet, "ACModel_BA_copy", "csv");
//benchmark_dissortative_swap(mnet, "ACModel_BA_copy", "csv");
}
}
hash_map<NodeSharedPtr,xyz_coordinates> circular(MLNetworkSharedPtr& mnet, double radius) {
hash_map<NodeSharedPtr,xyz_coordinates> pos;
double pi = 3.14159265358979323846;
if (mnet->get_actors()->size()==0) return pos;
double angle_offset = 360.0/mnet->get_actors()->size();
int i=0;
for (ActorSharedPtr a: *mnet->get_actors()) {
double degree = i*angle_offset;
double radians = degree*pi/180;
double x = std::cos(radians)*radius;
double y = std::sin(radians)*radius;
for (NodeSharedPtr n: *mnet->get_nodes(a)) {
pos[n].x = x;
pos[n].y = y;
pos[n].z = mnet->get_layers()->get_index(n->layer);
}
i++;
}
return pos;
}
property_matrix<ActorSharedPtr,LayerSharedPtr,double> actor_degree_property_matrix(const MLNetworkSharedPtr& mnet, edge_mode mode) {
property_matrix<ActorSharedPtr,LayerSharedPtr,double> P(mnet->get_actors()->size(),mnet->get_layers()->size(),0);
for (ActorSharedPtr actor: *mnet->get_actors()) {
for (LayerSharedPtr layer: *mnet->get_layers()) {
NodeSharedPtr node = mnet->get_node(actor,layer);
if (!node) P.set_na(actor,layer);
else P.set(actor,layer,mnet->neighbors(node,mode)->size());
}
}
return P;
}
// only works for multiplex networks (no inter-layer edges)
property_matrix<triad,LayerSharedPtr,bool> triangle_existence_property_matrix(const MLNetworkSharedPtr& mnet) {
long n = mnet->get_actors()->size();
property_matrix<triad,LayerSharedPtr,bool> P(n*(n-1)*(n-2)/6,mnet->get_layers()->size(),false);
for (LayerSharedPtr layer: *mnet->get_layers()) {
hash_set<NodeSharedPtr> processed1;
for (NodeSharedPtr n1: *mnet->get_nodes(layer)) {
processed1.insert(n1);
hash_set<NodeSharedPtr> processed2;
for (NodeSharedPtr n2: *mnet->neighbors(n1,INOUT)) {
if (processed1.count(n2)>0) continue;
processed2.insert(n2);
for (NodeSharedPtr n3: *mnet->neighbors(n2,INOUT)) {
if (processed1.count(n3)>0) continue;
if (processed2.count(n3)>0) continue;
if (mnet->get_edge(n3,n1)) {
triad t(n1->actor,n2->actor,n3->actor);
P.set(t,layer,true);
}
}
}
}
}
return P;
}
void test_transformations() {
test_begin("flattening");
std::cout << "Reading the multilayer network...";
MLNetworkSharedPtr mnet = read_multilayer("test/io2.mpx","mlnet 2",',');
ActorSharedPtr u1 = mnet->get_actor("U1");
ActorSharedPtr u2 = mnet->get_actor("U2");
ActorSharedPtr u3 = mnet->get_actor("U3");
std::unordered_set<LayerSharedPtr> layers;
layers.insert(mnet->get_layer("l1"));
layers.insert(mnet->get_layer("l2"));
std::cout << "done!" << std::endl;
std::cout << "Testing weighted flattening...";
LayerSharedPtr new_layer = flatten_weighted(mnet,"flat_weighted",layers,false,false);
if (!mnet->is_directed(new_layer,new_layer)) throw FailedUnitTestException("Layer should be directed");
if (mnet->get_nodes(new_layer).size() != 6) throw FailedUnitTestException("Wrong number of nodes");
if (mnet->get_edges(new_layer,new_layer).size() != 10) throw FailedUnitTestException("Wrong number of edges");
NodeSharedPtr n1 = mnet->get_node(u1,new_layer);
NodeSharedPtr n2 = mnet->get_node(u2,new_layer);
NodeSharedPtr n3 = mnet->get_node(u3,new_layer);
if (mnet->get_weight(n1,n2) != 1) throw FailedUnitTestException("Wrong weight, expected 1");
if (mnet->get_weight(n1,n3) != 2) throw FailedUnitTestException("Wrong weight, expected 2");
std::cout << "done!" << std::endl;
std::cout << "Testing or flattening...";
new_layer = flatten_unweighted(mnet,"flat_or",layers,false,false);
if (!mnet->is_directed(new_layer,new_layer)) throw FailedUnitTestException("Layer should be directed");
if (mnet->get_nodes(new_layer).size() != 6) throw FailedUnitTestException("Wrong number of nodes");
if (mnet->get_edges(new_layer,new_layer).size() != 10) throw FailedUnitTestException("Wrong number of edges");
std::cout << "done!" << std::endl;
test_end("flattening");
test_begin("projection");
/*
std::cout << "Reading the multilayer network...";
MLNetworkSharedPtr mnet_p = read_multilayer("test/io4.mln","interdependent",',');
LayerSharedPtr A = mnet_p->get_layer("A");
LayerSharedPtr P = mnet_p->get_layer("P");
std::cout << "done!" << mnet_p->to_string() << std::endl;
std::cout << "Testing clique projection...";
LayerSharedPtr projected_layer = project_unweighted(mnet_p,"flat_weighted",A,P);
if (mnet_p->is_directed(projected_layer,projected_layer)) throw FailedUnitTestException("Layer should be undirected");
if (mnet_p->get_nodes(projected_layer).size() != 5) throw FailedUnitTestException("Wrong number of nodes");
if (mnet_p->get_edges(projected_layer,projected_layer).size() != 5) throw FailedUnitTestException("Wrong number of edges");
std::cout << "done!" << std::endl;
*/
test_end("projection");
}
void test_evolution() {
{
test_begin("[Evolution Test R1] N1: larger independent, N2: smaller independent and with basic random graph evolution model");
// create multiplex network
MLNetworkSharedPtr mnet = MLNetwork::create("synt1");
LayerSharedPtr layer1 = mnet->add_layer("L1",true);
LayerSharedPtr layer2 = mnet->add_layer("L2",true);
// set evolution parameters
long num_of_steps = 100;
long num_of_actors = 1000;
std::vector<double> pr_no_event = {0.0,0.3};
std::vector<double> pr_internal_event = {1.0,1.0};
matrix<double> dependency = {{0, 1}, {1, 0}};
BAEvolutionModel ba(3,2);
UniformEvolutionModel ra(70);
std::vector<EvolutionModel*> evolution_model = {&ba, &ra};
evolve(mnet,num_of_steps,num_of_actors,pr_no_event,pr_internal_event,dependency,evolution_model);
//log(mnet->to_string());
//log("Edge Jaccard similarity: " + to_string(jaccard_similarity(mnet,layer1,layer2)));
//log("Assortativity: " + to_string(assortativity(mnet,layer1,layer2,OUT)));
}
{
test_begin("[Evolution Test R2] N1: larger independent, N2: smaller dependent on N1, both with basic random graph evolution model");
MLNetworkSharedPtr mnet = MLNetwork::create("synt1");
LayerSharedPtr layer1 = mnet->add_layer("L1",true);
LayerSharedPtr layer2 = mnet->add_layer("L2",true);
// set evolution parameters
long num_of_steps = 100;
long num_of_actors = 1000;
std::vector<double> pr_no_event = {0.0,0.3};
std::vector<double> pr_internal_event = {1.0,0.5};
matrix<double> dependency = {{0, 1}, {1, 0}};
UniformEvolutionModel ra(30);
std::vector<EvolutionModel*> evolution_model = {&ra, &ra};
evolve(mnet,num_of_steps,num_of_actors,pr_no_event,pr_internal_event,dependency,evolution_model);
//log(mnet->to_string());
//log("Edge Jaccard similarity: " + to_string(jaccard_similarity(mnet,layer1,layer2)));
//log("Assortativity: " + to_string(assortativity(mnet,layer1,layer2,OUT)));
}
/*
{
log("[Evolution Test 1] N1: larger independent, N2: smaller independent");
// create multiplex network
MultiplexNetwork mnet1;
for (int i=0; i<1000; i++) mnet1.addGlobalName("U"+std::to_string(i));
Network n1(true,false,false), n2(true,false,false);
network_id n1_1 = mnet1.addNetwork("N1",n1);
network_id n1_2 = mnet1.addNetwork("N2",n2);
// set evolution parameters
long num_of_steps = 100;
std::vector<double> pr_no_event = {0.0,0.3};
std::vector<double> pr_internal_event = {1.0,1.0};
vector<vector<double> > dependency = {{0, 1}, {1, 0}};
BAEvolutionModel ba(3,2);
std::vector<EvolutionModel*> evolution_model = {&ba, &ba};
evolve(mnet1,num_of_steps,pr_no_event,pr_internal_event,dependency,evolution_model);
log("Size 1: " + std::to_string(mnet1.getNetwork(n1_1).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_1).getNumEdges()) + " e");
log("Size 2: " + std::to_string(mnet1.getNetwork(n1_2).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_2).getNumEdges()) + " e");
int common_vertexes = 0;
for (vertex_id v: mnet1.getNetwork(n1_1).getVertexes()) {
global_identity id = mnet1.getGlobalIdentity(v, n1_1);
if (mnet1.containsVertex(id,n1_2))
common_vertexes++;
}
log("Common vertexes: " + std::to_string(common_vertexes));
log("Edge Jaccard similarity: " + std::to_string(network_jaccard_similarity(mnet1,n1_1,n1_2)));
}
{
log("[Evolution Test 2] N1: larger independent, N2: smaller dependent on N1");
// create multiplex network
MultiplexNetwork mnet1;
for (int i=0; i<1000; i++) mnet1.addGlobalName("U"+std::to_string(i));
Network n1(true,false,false), n2(true,false,false);
network_id n1_1 = mnet1.addNetwork("N1",n1);
network_id n1_2 = mnet1.addNetwork("N2",n2);
// set evolution parameters
long num_of_steps = 100;
std::vector<double> pr_no_event = {0.0,0.3};
std::vector<double> pr_internal_event = {1.0,0.7};
vector<vector<double> > dependency = {{0, 1}, {1, 0}};
BAEvolutionModel ba(3,2);
std::vector<EvolutionModel*> evolution_model = {&ba, &ba};
evolve(mnet1,num_of_steps,pr_no_event,pr_internal_event,dependency,evolution_model);
log("Size 1: " + std::to_string(mnet1.getNetwork(n1_1).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_1).getNumEdges()) + " e");
log("Size 2: " + std::to_string(mnet1.getNetwork(n1_2).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_2).getNumEdges()) + " e");
int common_vertexes = 0;
for (vertex_id v: mnet1.getNetwork(n1_1).getVertexes()) {
global_identity id = mnet1.getGlobalIdentity(v, n1_1);
if (mnet1.containsVertex(id,n1_2))
common_vertexes++;
}
log("Common vertexes: " + std::to_string(common_vertexes));
log("Edge Jaccard similarity: " + std::to_string(network_jaccard_similarity(mnet1,n1_1,n1_2)));
}
{
log("[Evolution Test 3] N1: larger independent, N2: smaller almost completely dependent on N1");
// create multiplex network
MultiplexNetwork mnet1;
for (int i=0; i<1000; i++) mnet1.addGlobalName("U"+std::to_string(i));
Network n1(true,false,false), n2(true,false,false);
network_id n1_1 = mnet1.addNetwork("N1",n1);
network_id n1_2 = mnet1.addNetwork("N2",n2);
// set evolution parameters
long num_of_steps = 100;
std::vector<double> pr_no_event = {0.0,0.3};
std::vector<double> pr_internal_event = {1.0,0.1};
vector<vector<double> > dependency = {{0, 1}, {1, 0}};
BAEvolutionModel ba(3,2);
std::vector<EvolutionModel*> evolution_model = {&ba, &ba};
evolve(mnet1,num_of_steps,pr_no_event,pr_internal_event,dependency,evolution_model);
log("Size 1: " + std::to_string(mnet1.getNetwork(n1_1).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_1).getNumEdges()) + " e");
log("Size 2: " + std::to_string(mnet1.getNetwork(n1_2).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_2).getNumEdges()) + " e");
int common_vertexes = 0;
for (vertex_id v: mnet1.getNetwork(n1_1).getVertexes()) {
global_identity id = mnet1.getGlobalIdentity(v, n1_1);
if (mnet1.containsVertex(id,n1_2))
common_vertexes++;
}
log("Common vertexes: " + std::to_string(common_vertexes));
log("Edge Jaccard similarity: " + std::to_string(network_jaccard_similarity(mnet1,n1_1,n1_2)));
}
{
log("[Evolution Test 4] N1: larger independent, N2: smaller dependent more on N1 than on N3, N3: smaller almost independent");
// create multiplex network
MultiplexNetwork mnet1;
for (int i=0; i<1000; i++) mnet1.addGlobalName("U"+std::to_string(i));
Network n1(true,false,false), n2(true,false,false), n3(true,false,false);
network_id n1_1 = mnet1.addNetwork("N1",n1);
network_id n1_2 = mnet1.addNetwork("N2",n2);
network_id n1_3 = mnet1.addNetwork("N3",n3);
// set evolution parameters
long num_of_steps = 100;
std::vector<double> pr_no_event = {0.0,0.2,0.2};
std::vector<double> pr_internal_event = {1.0,0.3,0.9};
vector<vector<double> > dependency = {{0, 1, 1}, {1, 0, .3}, {1, 1, 0}};
BAEvolutionModel ba(3,2);
std::vector<EvolutionModel*> evolution_model = {&ba, &ba, &ba};
evolve(mnet1,num_of_steps,pr_no_event,pr_internal_event,dependency,evolution_model);
log("Size 1: " + std::to_string(mnet1.getNetwork(n1_1).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_1).getNumEdges()) + " e");
log("Size 2: " + std::to_string(mnet1.getNetwork(n1_2).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_2).getNumEdges()) + " e");
log("Size 3: " + std::to_string(mnet1.getNetwork(n1_3).getNumVertexes()) + " v, " + std::to_string(mnet1.getNetwork(n1_3).getNumEdges()) + " e");
log("Edge Jaccard similarity (n1-n2): " + std::to_string(network_jaccard_similarity(mnet1,n1_1,n1_2)));
log("Edge Jaccard similarity (n1-n3): " + std::to_string(network_jaccard_similarity(mnet1,n1_1,n1_3)));
log("Edge Jaccard similarity (n2-n3): " + std::to_string(network_jaccard_similarity(mnet1,n1_3,n1_2)));
}
*/
{
test_begin("[Evolution Test 5] LARGER NETWORKS, COMMON ACTORS - N1: larger independent, N2: smaller dependent more on N1 than on N3, N3: smaller almost independent");
// create multiplex network
MLNetworkSharedPtr mnet = MLNetwork::create("synt1");
LayerSharedPtr layer1 = mnet->add_layer("L1",true);
LayerSharedPtr layer2 = mnet->add_layer("L2",true);
LayerSharedPtr layer3 = mnet->add_layer("L3",true);
// set evolution parameters
long num_of_steps = 1000;
long num_of_actors = 10000;
std::vector<double> pr_no_event = {0.0,0.3,0.3};
std::vector<double> pr_internal_event = {1,0,0.9};
matrix<double> dependency = {{0, 0, 0}, {1, 0, 0}, {1, 0, 0}};
BAEvolutionModel ba(3,2);
std::vector<EvolutionModel*> evolution_model = {&ba, &ba, &ba};
evolve(mnet,num_of_steps,num_of_actors,pr_no_event,pr_internal_event,dependency,evolution_model);
//log(mnet->to_string());
//log("Edge Jaccard similarity 1-2: " + to_string(jaccard_similarity(mnet,layer1,layer2)));
//log("Edge Jaccard similarity 1-3: " + to_string(jaccard_similarity(mnet,layer1,layer3)));
//log("Edge Jaccard similarity 2-3: " + to_string(jaccard_similarity(mnet,layer2,layer3)));
//log("Assortativity 1-2: " + to_string(assortativity(mnet,layer1,layer2,OUT)));
//log("Assortativity 1-3: " + to_string(assortativity(mnet,layer1,layer3,OUT)));
//log("Assortativity 2-3: " + to_string(assortativity(mnet,layer2,layer3,OUT)));
}
}
hashtable<NodeSharedPtr,coordinates> multiforce(MLNetworkSharedPtr mnet, double width, double length, int iterations) {
hashtable<NodeSharedPtr,coordinates> pos;
hashtable<NodeSharedPtr,coordinates> disp;
double t = std::sqrt(width*width+length*length);
double area = width*length;
double k = std::sqrt(area/mnet->get_actors().size());
for (ActorSharedPtr a: mnet->get_actors()) {
double y = drand()*length-length/2;
double x = drand()*width-width/2;
for (NodeSharedPtr n: mnet->get_nodes(a)) {
pos[n].y = x;
pos[n].x = y;
pos[n].z = mnet->get_layers().get_index(n->layer->id);
}
}
for (int i=0; i<iterations; i++) {
// calculate repulsive forces
for (LayerSharedPtr l: mnet->get_layers()) {
for (NodeSharedPtr v: mnet->get_nodes(l)) {
disp[v].x = 0;
disp[v].y = 0;
for (NodeSharedPtr u: mnet->get_nodes(l)) {
if (u == v) continue;
coordinates Delta;
Delta.x = pos[v].x - pos[u].x;
Delta.y = pos[v].y - pos[u].y;
double DeltaNorm = std::sqrt(Delta.x*Delta.x+Delta.y*Delta.y);
disp[v].x = disp[v].x + Delta.x/DeltaNorm*fr(DeltaNorm,k);
disp[v].y = disp[v].y + Delta.y/DeltaNorm*fr(DeltaNorm,k);
}
}
}
// calculate attractive forces inside each layer for ((u, v) ∈ E) do
for (LayerSharedPtr l: mnet->get_layers()) {
for (EdgeSharedPtr e: mnet->get_edges(l,l)) {
NodeSharedPtr v = e->v1;
NodeSharedPtr u = e->v2;
coordinates Delta;
Delta.x = pos[v].x - pos[u].x;
Delta.y = pos[v].y - pos[u].y;
double DeltaNorm = std::sqrt(Delta.x*Delta.x+Delta.y*Delta.y);
disp[v].x = disp[v].x - Delta.x/DeltaNorm*fain(DeltaNorm,k);
disp[v].y = disp[v].y - Delta.y/DeltaNorm*fain(DeltaNorm,k);
disp[u].x = disp[u].x + Delta.x/DeltaNorm*fain(DeltaNorm,k);
disp[u].y = disp[u].y + Delta.y/DeltaNorm*fain(DeltaNorm,k);
}
}
// calculate attractive forces across layers
for (ActorSharedPtr a: mnet->get_actors()) {
for (NodeSharedPtr v: mnet->get_nodes(a)) {
for (NodeSharedPtr u: mnet->get_nodes(a)) {
if (v == u) continue;
coordinates Delta;
Delta.x = pos[v].x - pos[u].x;
Delta.y = pos[v].y - pos[u].y;
double DeltaNorm = std::sqrt(Delta.x*Delta.x+Delta.y*Delta.y);
disp[v].x = disp[v].x - Delta.x/DeltaNorm*fainter(DeltaNorm,k);
disp[v].y = disp[v].y - Delta.y/DeltaNorm*fainter(DeltaNorm,k);
disp[u].x = disp[u].x + Delta.x/DeltaNorm*fainter(DeltaNorm,k);
disp[u].y = disp[u].y + Delta.y/DeltaNorm*fainter(DeltaNorm,k);
}
}
}
// assign new positions
for (NodeSharedPtr v: mnet->get_nodes()) {
double dispNorm = std::sqrt(disp[v].x*disp[v].x+disp[v].y*disp[v].y);
pos[v].x = pos[v].x + (disp[v].x/dispNorm);//*std::min(dispNorm,t);
pos[v].y = pos[v].y + (disp[v].y/dispNorm);//*std::min(dispNorm,t);
//pos[v].x = std::min(width/2, std::max(-width/2, pos[v].x));
//pos[v].y = std::min(length/2, std::max(-length/2, pos[v].y));
}
// reduce the temperature
t -= (t-1)/(iterations+1);
}
// DEBUG PRINT
double min_x = 100000000;
double min_y = 100000000;
double max_x = -100000000;
double max_y = -100000000;
for (NodeSharedPtr v: mnet->get_nodes()) {
if (pos[v].x < min_x) min_x = pos[v].x;
if (pos[v].y < min_y) min_y = pos[v].y;
if (pos[v].x > max_x) max_x = pos[v].x;
if (pos[v].y > max_y) max_y = pos[v].y;
}
std::cout << "plot(c(),xlim=c(" << min_x << "," << max_x << "),ylim=c(" << min_y << "," << max_y << "))" << std::endl;
//std::cout << "legend(4,4,0:" << (iterations-1) << ",fill=1:" << (iterations) << ")" << std::endl;
for (NodeSharedPtr n: mnet->get_nodes()) {
std::cout << "points(" << pos[n].x << "," << pos[n].y << ")" << std::endl;
}
for (EdgeSharedPtr e: mnet->get_edges()) {
std::cout << "lines(c(" << pos[e->v1].x << "," << pos[e->v2].x << "),c(" << pos[e->v1].y << "," << pos[e->v2].y << "))" << std::endl;
}
//
return pos;
}
