WeightedTriangleLinkPredictor::WeightedTriangleLinkPredictor( const WeightedNetwork& network, int selection = -1 ) : LinkPredictor( network ), selection( selection ) {
if( this->selection > -1 ) {
return;
}
weight_t allCounts[graphlets] = {0};
for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) {
const neighbor_set_t& outNeighbors = network.outNeighbors( vertex );
for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); ++neighborIterator ) {
const vertex_t neighbor = neighborIterator->first;
weight_t vCounts[graphlets] = {0};
this->edgeProfile( vertex, neighbor, vCounts );
for( size_t i = 0; i < graphlets; ++i ) {
allCounts[i] += vCounts[i];
}
}
}
for( size_t i = 0; i < graphlets; ++i ) {
this->counts[i] = (weight_t)allCounts[i] / network.edgeCount();
}
}
int main( int argc, char* argv[] ) {
char c;
while( (c = getopt( argc, argv, "+h" )) >= 0 ) {
if( c == 'h' ) {
usage( argv[0] );
return 0;
}
}
if( argc != optind + 1 ) {
usage( argv[0] );
return 1;
}
unsigned int distance = atoi( argv[optind++] );
WeightedNetwork network = WeightedNetwork::readNetwork( cin );
for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) {
WeightedNetwork snowball = network.snowballSample( vertex, distance ).removeIsolates();
cout << snowball.vertexCount() << " " << snowball.edgeCount() << "\n";
}
return 0;
}