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Generating Graph Snapshots from Streaming Edge Data

Authors:

Brian Gallagher, and

Kevin RoundyAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

Pages 109 - 110

https://doi.org/10.1145/2872518.2889398

Published: 11 April 2016 Publication History

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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.

References

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R. S. Caceres. Temporal Scale of Dynamic Networks. PhD thesis, University of Illinois at Chicago, 2013.

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J. Kiernan and E. Terzi. Constructing comprehensive summaries of large event sequences. TKDE, 3(4):21:1--21:31, 2009.

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Cited By

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Wendt JField RPhillips CPrasadan AWilson TSoundarajan SBhowmick S(2023)Partitioning Communication Streams Into Graph SnapshotsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.322361410:2(809-826)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TNSE.2022.3223614
Ding JWang TCheng RJiao LWu JBai J(2023)Community evolution prediction based on a self-adaptive timeframe in social networksKnowledge-Based Systems10.1016/j.knosys.2023.110687275(110687)Online publication date: Sep-2023
https://doi.org/10.1016/j.knosys.2023.110687
Xie YWang WShao MLi TYu Y(2023)Multi-view Change Point Detection in Dynamic NetworksInformation Sciences10.1016/j.ins.2023.01.118Online publication date: Feb-2023
https://doi.org/10.1016/j.ins.2023.01.118
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Index Terms

Generating Graph Snapshots from Streaming Edge Data
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social networks
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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LLNL
NSF
DTRA

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WWW '16

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IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

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WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Wendt JField RPhillips CPrasadan AWilson TSoundarajan SBhowmick S(2023)Partitioning Communication Streams Into Graph SnapshotsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.322361410:2(809-826)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TNSE.2022.3223614
Ding JWang TCheng RJiao LWu JBai J(2023)Community evolution prediction based on a self-adaptive timeframe in social networksKnowledge-Based Systems10.1016/j.knosys.2023.110687275(110687)Online publication date: Sep-2023
https://doi.org/10.1016/j.knosys.2023.110687
Xie YWang WShao MLi TYu Y(2023)Multi-view Change Point Detection in Dynamic NetworksInformation Sciences10.1016/j.ins.2023.01.118Online publication date: Feb-2023
https://doi.org/10.1016/j.ins.2023.01.118
Orman G(2022)Dynamic network modelling with similarity based aggregation algorithmComputer Science and Information Systems10.2298/CSIS211215012O19:2(1023-1046)Online publication date: 2022
https://doi.org/10.2298/CSIS211215012O
Jin DKim SRossi RKoutra DSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)On Generalizing Static Node Embedding to Dynamic SettingsProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498428(410-420)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498428
Byun J(2022)Enabling Time-Centric Computation for Efficient Temporal Graph Traversals From Multiple SourcesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.300567234:4(1751-1762)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TKDE.2020.3005672
Hilsabeck TArastuie MXu K(2022)A hybrid adjacency and time-based data structure for analysis of temporal networksApplied Network Science10.1007/s41109-022-00489-57:1Online publication date: 5-Jul-2022
https://doi.org/10.1007/s41109-022-00489-5
Fan WLi YLiu MLu C(2022)Making graphs compact by lossless contractionThe VLDB Journal10.1007/s00778-022-00731-732:1(49-73)Online publication date: 19-Feb-2022
https://doi.org/10.1007/s00778-022-00731-7
Chiappori ACazabet R(2022)Quantitative Evaluation of Snapshot Graphs for the Analysis of Temporal NetworksComplex Networks & Their Applications X10.1007/978-3-030-93409-5_47(566-577)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-93409-5_47
Wang YLi PBai CLeskovec J(2021)TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction NetworksProceedings of the Web Conference 202110.1145/3442381.3450096(693-705)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450096
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