research-article

A viewpoint-based approach for interaction graph analysis

Authors:

Srinivasan ParthasarathyAuthors Info & Claims

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 79 - 88

https://doi.org/10.1145/1557019.1557035

Published: 28 June 2009 Publication History

Abstract

Recent innovations have resulted in a plethora of social applications on the Web, such as blogs, social networks, and community photo and video sharing applications. Such applications can typically be represented as evolving interaction graphs with nodes denoting entities and edges representing their interactions. The study of entities and communities and how they evolve in such large dynamic graphs is both important and challenging.

While much of the past work in this area has focused on static analysis, more recently researchers have investigated dynamic analysis. In this paper, in a departure from recent efforts, we consider the problem of analyzing patterns and critical events that affect the dynamic graph from the viewpoint of a single node, or a selected subset of nodes. Defining and extracting a relevant viewpoint neighborhood efficiently, while also quantifying the key relationships among nodes involved are the key challenges we address. We also examine the evolution of viewpoint neighborhoods for different entities over time to identify key structural and behavioral transformations that occur.

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Cited By

Gu KLiu DWang K(2019)Social Community Detection Scheme Based on Social-Aware in Mobile Social NetworksIEEE Access10.1109/ACCESS.2019.29561497(173407-173418)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2956149
Vedula NParthasarathy SShalin VFox PMcGuinness DPoirer LBoldi PKinder-Kurlanda K(2017)Predicting Trust Relations Within a Social NetworkProceedings of the 2017 ACM on Web Science Conference10.1145/3091478.3091494(53-62)Online publication date: 25-Jun-2017
https://dl.acm.org/doi/10.1145/3091478.3091494
Vedula NParthasarathy SKostkova PGrasso FCastillo CMejova YBosman A(2017)Emotional and Linguistic Cues of Depression from Social MediaProceedings of the 2017 International Conference on Digital Health10.1145/3079452.3079465(127-136)Online publication date: 2-Jul-2017
https://dl.acm.org/doi/10.1145/3079452.3079465
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Index Terms

A viewpoint-based approach for interaction graph analysis
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

June 2009

1426 pages

ISBN:9781605584959

DOI:10.1145/1557019

General Chairs:
John Elder
Elder Research, Inc., USA
,
Françoise Soulié Fogelman
KXEN, France
,
Program Chairs:
Peter Flach
University of Bristol, UK
,
Mohammed Zaki
RPI, USA

Copyright © 2009 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: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28 - July 1, 2009

Paris, France

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

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Cited By

Gu KLiu DWang K(2019)Social Community Detection Scheme Based on Social-Aware in Mobile Social NetworksIEEE Access10.1109/ACCESS.2019.29561497(173407-173418)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2956149
Vedula NParthasarathy SShalin VFox PMcGuinness DPoirer LBoldi PKinder-Kurlanda K(2017)Predicting Trust Relations Within a Social NetworkProceedings of the 2017 ACM on Web Science Conference10.1145/3091478.3091494(53-62)Online publication date: 25-Jun-2017
https://dl.acm.org/doi/10.1145/3091478.3091494
Vedula NParthasarathy SKostkova PGrasso FCastillo CMejova YBosman A(2017)Emotional and Linguistic Cues of Depression from Social MediaProceedings of the 2017 International Conference on Digital Health10.1145/3079452.3079465(127-136)Online publication date: 2-Jul-2017
https://dl.acm.org/doi/10.1145/3079452.3079465
Yin HCui BLu HZhao L(2016)Expert team finding for review assignment2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI.2016.7932314(1-8)Online publication date: Nov-2016
https://doi.org/10.1109/TAAI.2016.7932314
Wang CChakrabarti KHe YGanjam KChen ZBernstein PGangemi ALeonardi SPanconesi A(2015)Concept Expansion Using Web TablesProceedings of the 24th International Conference on World Wide Web10.1145/2736277.2741644(1198-1208)Online publication date: 18-May-2015
https://dl.acm.org/doi/10.1145/2736277.2741644
Ruchansky NBonchi FGarcía-Soriano DGullo FKourtellis NSellis TDavidson SIves Z(2015)The Minimum Wiener Connector ProblemProceedings of the 2015 ACM SIGMOD International Conference on Management of Data10.1145/2723372.2749449(1587-1602)Online publication date: 27-May-2015
https://dl.acm.org/doi/10.1145/2723372.2749449
Xu HYang YWang LLiu W(2013)Node Classification in Social Network via a Factor Graph ModelAdvances in Knowledge Discovery and Data Mining10.1007/978-3-642-37453-1_18(213-224)Online publication date: 2013
https://doi.org/10.1007/978-3-642-37453-1_18
Wang GZhao YShi XYu PYang QAgarwal DPei J(2012)Magnet community identification on social networksProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339627(588-596)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2339530.2339627
Takaffoli MFagnan JSangi FZaiane O(2011)Tracking changes in dynamic information networks2011 International Conference on Computational Aspects of Social Networks (CASoN)10.1109/CASON.2011.6085925(94-101)Online publication date: Oct-2011
https://doi.org/10.1109/CASON.2011.6085925
Takaffoli MSangi FFagnan JZäıane O(2011)Community Evolution Mining in Dynamic Social NetworksProcedia - Social and Behavioral Sciences10.1016/j.sbspro.2011.07.05522(49-58)Online publication date: 2011
https://doi.org/10.1016/j.sbspro.2011.07.055
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