document: Mobile call graphs : beyond power-law and lognormal distributions We analyze a massive social network , gathered from the records of a large mobile phone operator , with more than a million users and tens of millions of calls . We examine the distributions of the number of phone calls per customer ; the total talk minutes per customer ; and the distinct number of calling partners per customer . We find that these distributions are skewed , and that they significantly deviate from what would be expected by power-law and lognormal distributions . To analyze our observed distributions ( of number of calls , distinct call partners , and total talk time ) , we propose PowerTrack , a method which fits a lesser known but more suitable distribution , namely the Double Pareto LogNormal ( DPLN ) distribution , to our data and track its parameters over time . Using PowerTrack , we find that our graph changes over time in a way consistent with a generative process that naturally results in the DPLN distributions we observe . Furthermore , we show that this generative process lends itself to a natural and appealing social wealth interpretation in the context of social networks such as ours . We discuss the application of those results to our model and to forecasting .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O