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this is joint work with Monique a serosa MIT Lincoln labs and Iamp;#39;ll be talking about a task driven approach to time scale detection and dynamic networks so whatamp;#39;s the question there it is itamp;#39;s very cut off but okay so weamp;#39;re given as input dynamic Network which consists of a stream of time-stamped edges which you can see in the bottom row here and for any or at least many data mining tasks one of the first things you need to choose is a bin size or what Iamp;#39;ll call window size where all edges that occur within that bin will be placed into one graph and then youamp;#39;ll get a sequence of graphs as you can see in the second and third rows there now if your bin size is very is too big at the top then you lose all temporal information which youamp;#39;d like to take advantage of on the other hand if your bin size is too small at the bottom then thereamp;#39;s going to be lots of spurious correlations noise and you donamp;#39;t get to see the effect
