Article

An event-based framework for characterizing the evolutionary behavior of interaction graphs

Authors:

Srinivasan Parthasarathy,

Duygu UcarAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 913 - 921

https://doi.org/10.1145/1281192.1281290

Published: 12 August 2007 Publication History

Abstract

Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these graphs from a static point of view. The study of the evolution of these graphs over time can provide tremendous insight on the behavior of entities, communities and the flow of information among them. In this work, we present an event-based characterization of critical behavioral patterns for temporally varying interaction graphs. We use non-overlapping snapshots of interaction graphs and develop a framework for capturing and identifying interesting events from them. We use these events to characterize complex behavioral patterns of individuals and communities over time. We demonstrate the application of behavioral patterns for the purposes of modeling evolution, link prediction and influence maximization. Finally, we present a diffusion model for evolving networks, based on our framework.

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Index Terms

An event-based framework for characterizing the evolutionary behavior of interaction graphs
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 August 2007

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

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KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1109/CEI60616.2023.10528135
Qin HLi RYuan YWang GQin LZhang Z(2022)Mining Bursting Core in Large Temporal GraphsProceedings of the VLDB Endowment10.14778/3565838.356584515:13(3911-3923)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.14778/3565838.3565845
Qin HLi RWang GHuang XYuan YYu J(2022)Mining Stable Communities in Temporal Networks by Density-Based ClusteringIEEE Transactions on Big Data10.1109/TBDATA.2020.29748498:3(671-684)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TBDATA.2020.2974849
Chen DNie MWang JKong YWang DHuang X(2021)Community Detection Based on Graph Representation Learning in Evolutionary NetworksApplied Sciences10.3390/app1110449711:10(4497)Online publication date: 14-May-2021
https://doi.org/10.3390/app11104497
Jheng GChen YLiang H(2021)Evolution pattern mining on dynamic social networkThe Journal of Supercomputing10.1007/s11227-020-03534-1Online publication date: 4-Jan-2021
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Su XCheng JYang HLeng MZhang WChen X(2020)IncNSA: detecting communities incrementally from time-evolving networks based on node similarityInternational Journal of Modern Physics C10.1142/S0129183120500941Online publication date: 17-Mar-2020
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