Generating Graph Snapshots from Streaming Edge Data

Sucheta Soundarajan, Acar Tamersoy, Elias B. Khalil, Tina Eliassi-Rad, Duen Horng Chau, Brian Gallagher, Kevin Roundy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationWWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages109-110
Number of pages2
ISBN (Electronic)9781450341448
DOIs
StatePublished - Apr 11 2016
Event25th International Conference on World Wide Web, WWW 2016 - Montreal, Canada
Duration: May 11 2016May 15 2016

Publication series

NameWWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web

Conference

Conference25th International Conference on World Wide Web, WWW 2016
Country/TerritoryCanada
CityMontreal
Period5/11/165/15/16

Keywords

  • aggregating edge streams
  • time-evolving networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Fingerprint

Dive into the research topics of 'Generating Graph Snapshots from Streaming Edge Data'. Together they form a unique fingerprint.

Cite this