Generating Graph Snapshots from Streaming Edge Data

S. Soundarajan, A. Tamersoy, E.B. Khalil, T. Eliassi-Rad, D.H. Chau, B. Gallagher, K. Roundy
Proceedings of the 25th International World Wide Web Conference
Poster Track, Montreal, Canada, April 2016
April 11, 2016


We study the  problem of determining the proper aggregation granularity for a stream of  time-stamped edges. Such streams are used to build time-evolving networks,  which are subsequently used to study topics such as network growth.  Currently, aggregation lengths are chosen arbitrarily, based on intuition or  convenience. We describe ADAGE, which detects the appropriate aggregation  intervals from streaming edges and outputs a sequence of structurally mature  graphs. We demonstrate the value of ADAGE in automatically finding the  appropriate aggregation intervals on edge streams for belief propagation to  detect malicious files and machines.

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