Information Sciences: an International Journal
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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%).