docs: 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