document: Large human communication networks : patterns and a utility-driven generator Given a real , and weighted person-to-person network which changes over time , what can we say about the cliques that it contains ? Do the incidents of communication , or weights on the edges of a clique follow any pattern ? Real , and in-person social networks have many more triangles than chance would dictate . As it turns out , there are many more cliques than one would expect , in surprising patterns . In this paper , we study massive real-world social networks formed by direct contacts among people through various personal communication services , such as Phone-Call , SMS , IM etc. . The contributions are the following : ( a ) we discover surprising patterns with the cliques , ( b ) we report power-laws of the weights on the edges of cliques , ( c ) our real networks follow these patterns such that we can trust them to spot outliers and finally , ( d ) we propose the first utility-driven graph generator for weighted time-evolving networks , which match the observed patterns . Our study focused on three large datasets , each of which is a different type of communication service , with over one million records , and spans several months of activity .
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