/**
* Indicates if all ties are reciprocated with the same value.
*/
bool OneModeNetwork::symmetric() const {
// The current implementation is linear in the total number of ties.
// The time complexity can be reduced to a constant by maintaining
// the number of non-symmetric ties.
// Assume the network is symmetric until we can disprove it.
bool rc = true;
// Test the incoming and outgoing ties of each actor in turn.
for (int i = 0; i < this->n() && rc; i++) {
if (this->outDegree(i) == this->inDegree(i)) {
IncidentTieIterator outIter = this->outTies(i);
IncidentTieIterator inIter = this->inTies(i);
// No need to test both iterators for validity, as the numbers
// of incoming and outgoing ties are the same.
while (outIter.valid() && rc) {
if (outIter.actor() != inIter.actor()
|| outIter.value() != inIter.value()) {
// Found a mismatch.
rc = false;
}
outIter.next();
inIter.next();
}
} else {
// The numbers of incoming and outgoing ties differ, which
// destroys the symmetry immediately.
rc = false;
}
}
return rc;
}
/**
* Returns the statistic corresponding to the given ego as part of
* the endowment function with respect to the initial values of a
* behavior variable and the current values.
*/
double SimilarityEffect::egoEndowmentStatistic(int ego, const int * difference,
double * currentValues)
{
if (this->lalterPopularity)
{
throw runtime_error(string("endowmentStatistic not implemented for") +
"average similarity x popularity alter effect and " +
"total similarity x popularity alter effect.");
}
double statistic = 0;
const Network * pNetwork = this->pNetwork();
double similarityMean = this->similarityMean();
if (!this->missing(this->period(), ego) &&
!this->missing(this->period() + 1, ego))
{
if (difference[ego] > 0)
{
if (pNetwork->outDegree(ego))
{
double thisStatistic = 0;
for (IncidentTieIterator iter = pNetwork->outTies(ego);
iter.valid();
iter.next())
{
if (!this->missing(this->period(), iter.actor()) &&
!this->missing(this->period() + 1, iter.actor()))
{
double alterValue = currentValues[iter.actor()];
double range = this->range();
thisStatistic += iter.value() *
(1.0 - fabs(alterValue - currentValues[ego]) /
range);
thisStatistic -= similarityMean;
}
}
if (this->laverage)
{
thisStatistic /= pNetwork->outDegree(ego);
}
if (this->legoPopularity)
{
thisStatistic *= pNetwork->inDegree(ego);
}
statistic = thisStatistic;
// do the same using the current state plus difference
// in i's value rather than current state and subtract it.
// not sure whether this is correct.
thisStatistic = 0;
for (IncidentTieIterator iter = pNetwork->outTies(ego);
iter.valid();
iter.next())
{
if (!this->missing(this->period(), iter.actor()) &&
!this->missing(this->period() + 1, iter.actor()))
{
double alterValue = currentValues[iter.actor()] +
difference[iter.actor()];
double range = this->range();
thisStatistic += iter.value() *
(1.0 - fabs(alterValue - (difference[ego] +
currentValues[ego]))
/ range);
thisStatistic -= similarityMean;
}
}
if (this->laverage)
{
thisStatistic /= pNetwork->outDegree(ego);
}
if (this->legoPopularity)
{
thisStatistic *= pNetwork->inDegree(ego);
}
statistic -= thisStatistic;
}
}
}
return statistic;
}