def adamic(brands, exemplars):
    """ Return the average Adamic/Adar similarity between a brand's followers
    and the followers of each exemplar. We approximate the number of followed
    accounts per user by only considering those in our brand set."""
    print('adamic deprecated...requires loading all brands in memory.')
    return
    degrees = compute_log_degrees(brands, exemplars)
    scores = {}
    exemplar_sums = dict([(exemplar, sum(degrees[z] for z in exemplars[exemplar])) for exemplar in exemplars])

    for brand in sorted(brands):
        brand_sum = sum(degrees[z] for z in brands[brand])
        total = 0.
        for exemplar in exemplars:
            total += sum(degrees[z] for z in brands[brand] & exemplars[exemplar]) / (brand_sum + exemplar_sums[exemplar])
        scores[brand] = total / len(exemplars)
    return scores