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Visualizing the impact of time series data for predicting user interactions

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ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 1477 - 1478

https://doi.org/10.1145/2492517.2492601

Published: 25 August 2013 Publication History

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Abstract

In recent years the importance of user interactions has been recognized in a variety of research contexts. There is a variety of algorithms for modeling these in social graphs; in particular, we distinguish static and dynamic relations. In contrast to static graphs in which the networks do not change over time, the underlying relation is changing frequently in various contexts. This should be reflected by a time dependent social neighborhood of users. In this paper, we present a new and intuitive visualization concept for the histories of user interactions. We derive association rules and visualize these using heatmaps. We demonstrate the impact of the presented approach by several examples utilizing real-world data -- using the well known twitter dump of 2009.

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Cited By

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Liao XNazir SShen JShen BKhan S(2021)Rough Set Approach toward Data Modelling and User Knowledge for Extracting InsightsComplexity10.1155/2021/78154182021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/7815418
Liao XNazir SZhou YShafiq MQi X(2021)User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic‐Based ApproachComplexity10.1155/2021/66290862021:1Online publication date: 26-Feb-2021
https://doi.org/10.1155/2021/6629086
Zhang ZZhao WYang JParis CNepal S(2019)Learning Influence Probabilities and Modelling Influence Diffusion in TwitterCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316701(1087-1094)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316701

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Published In

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

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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Association for Computing Machinery

New York, NY, United States

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Published: 25 August 2013

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SIGKDD

ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

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Liao XNazir SShen JShen BKhan S(2021)Rough Set Approach toward Data Modelling and User Knowledge for Extracting InsightsComplexity10.1155/2021/78154182021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/7815418
Liao XNazir SZhou YShafiq MQi X(2021)User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic‐Based ApproachComplexity10.1155/2021/66290862021:1Online publication date: 26-Feb-2021
https://doi.org/10.1155/2021/6629086
Zhang ZZhao WYang JParis CNepal S(2019)Learning Influence Probabilities and Modelling Influence Diffusion in TwitterCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316701(1087-1094)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316701
Macek BAtzmueller MStumme G(2014)Predicting the stability of user interaction ties in TwitterProceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business10.1145/2637748.2638428(1-8)Online publication date: 16-Sep-2014
https://dl.acm.org/doi/10.1145/2637748.2638428

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Visualizing large time-series data on very small screens

Visualizing time-oriented data-A systematic view

Visualizing Network Data

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