Using a Pheromone Mechanism to Estimate the Size of Unstructured Networks

Accurately estimating network size is essential in unstructured networks. In previous studies, proposed sampling mechanisms for estimating network sizes assumed the probability that a peer is sampled is proportional to the number of its neighbors. This assumption leads to a sampling bias in favor of peers with many neighbors - something that commonly occurs in power law networks. To reduce this sampling bias, we propose a pheromone mechanism, that calibrates sampling probability by the amount of pheromone. This mechanism can be adapted to existing size-estimation techniques. Our empirical studies show that by adapting the pheromone mechanism, most size-estimation techniques can be significantly improved (in some cases, by more than 100%).