int main( int argc, char* argv[]) {
if(argc != 2) {
std::cerr << "Usage : ./main.out <edge_file>" << std::endl;
exit(1);
}
Network network;
std::ifstream fin(argv[1]);
std::cerr << "Loading input file" << std::endl;
network.LoadFile( fin );
bool is_weighted = network.IsWeighted();
if( ! is_weighted ) {
std::cerr << "All the link weights are 1. Analyze the network as a non-weighted network." << std::endl;
}
std::pair<double,double> fc;
if( is_weighted ) {
std::cerr << "Conducting percolation analysis" << std::endl;
std::ofstream lrp("link_removal_percolation.dat");
lrp << "#fraction weak_link_removal_lcc susceptibility strong_link_removal_lcc susceptibility" << std::endl;
fc = network.AnalyzeLinkRemovalPercolationVariableAccuracy( 0.02, 0.005, lrp );
lrp.flush();
}
std::cerr << "Calculating local clustering coefficients" << std::endl;
network.CalculateLocalCCs();
if( is_weighted ) {
std::cerr << "Calculating overlaps" << std::endl;
network.CalculateOverlaps();
}
std::cerr << "Calculating degree distribution" << std::endl;
std::ofstream dd("degree_distribution.dat");
const auto degree_distribution = network.DegreeDistribution();
for(const auto& f : degree_distribution ) {
dd << f.first << ' ' << f.second << std::endl;
}
dd.flush();
if( is_weighted ) {
std::cerr << "Calculating link weight distribution" << std::endl;
// double edge_weight_bin_size = 1.0;
std::ofstream ewd("edge_weight_distribution.dat");
for(const auto& f : network.EdgeWeightDistributionLogBin() ) {
ewd << f.first << ' ' << f.second << std::endl;
}
ewd.flush();
}
std::map<double, size_t> strength_distribution;
if( is_weighted ) {
std::cerr << "Calculating node strength distribution" << std::endl;
double avg_s = network.AverageEdgeWeight() * network.AverageDegree();
double strength_bin_size = avg_s * 0.01;
std::ofstream sd("strength_distribution.dat");
strength_distribution = network.StrengthDistribution(strength_bin_size);
for(const auto& f :strength_distribution) {
sd << f.first << ' ' << f.second << std::endl;
}
sd.flush();
}
std::cerr << "Calculating c(k)" << std::endl;
std::ofstream cc_d("cc_degree_correlation.dat");
for(const auto& f : network.CC_DegreeCorrelation() ) {
cc_d << f.first << ' ' << f.second << std::endl;
}
cc_d.flush();
if( is_weighted ) {
std::cerr << "Calculating s(k)" << std::endl;
std::ofstream sdc("strength_degree_correlation.dat");
for(const auto& f : network.StrengthDegreeCorrelation() ) {
sdc << f.first << ' ' << f.second << std::endl;
}
sdc.flush();
}
std::cerr << "Calculating k_nn(k)" << std::endl;
std::ofstream ndc("neighbor_degree_correlation.dat");
for(const auto& f : network.NeighborDegreeCorrelation() ) {
ndc << f.first << ' ' << f.second << std::endl;
}
ndc.flush();
if( is_weighted ) {
std::cerr << "Calculating O(w)" << std::endl;
std::ofstream owc("overlap_weight_correlation.dat");
for(const auto& f : network.OverlapWeightCorrelationLogBin() ) {
owc << f.first << ' ' << f.second << std::endl;
}
owc.flush();
}
std::cerr << "Calculating scalar values" << std::endl;
std::ofstream fout("_output.json");
fout << "{" << std::endl;
fout << " \"NumNodes\": " << network.NumNodes() << ',' << std::endl;
fout << " \"NumEdges\": " << network.NumEdges() << ',' << std::endl;
fout << " \"AverageDegree\": " << network.AverageDegree() << ',' << std::endl;
fout << " \"Assortativity\": " << network.PCC_k_knn() << ',' << std::endl;
fout << " \"ArgMax_Pk\": " << ArgMax( degree_distribution ) << ',' << std::endl;
fout << " \"ClusteringCoefficient\": " << network.ClusteringCoefficient() << ',' << std::endl;
fout << " \"PCC_C_k\": " << network.PCC_C_k();
if( is_weighted ) {
fout << ',' << std::endl;
double ave_w = network.AverageEdgeWeight();
fout << " \"AverageEdgeWeight\": " << ave_w << ',' << std::endl;
double ave_k = network.AverageDegree();
fout << " \"AverageStrength\": " << ave_w * ave_k << ',' << std::endl;
double argmax_ps = ArgMax( strength_distribution );
fout << " \"ArgMax_Ps\": " << argmax_ps << ',' << std::endl;
fout << " \"Normalized_ArgMax_Ps\": " << argmax_ps / (ave_w*ave_k) << ',' << std::endl;
fout << " \"PCC_s_k\": " << network.PCC_s_k() << ',' << std::endl;
fout << " \"AverageOverlap\": " << network.AverageOverlap() << ',' << std::endl;
fout << " \"PCC_O_w\": " << network.PCC_O_w() << ',' << std::endl;
fout << " \"Fc_Ascending\": " << fc.first << ',' << std::endl;
fout << " \"Fc_Descending\": " << fc.second << ',' << std::endl;
fout << " \"Delta_Fc\": " << fc.second - fc.first << std::endl;
}
fout << "}" << std::endl;
return 0;
}
