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abstract

Visualizing Personalized Multifaceted ad-hoc Social Network

Published: 13 July 2020 Publication History

Abstract

We demonstrate a novel personalized, multifaceted entity-relation graph visualization. Since entities we are linked to are part of our lives (and profile) and help to understand who we are and what are we interested we enable users to explore entities they are linked to in a specific context. For that purpose, we adapt the typed entity-relation graph (profile) concept. In the context of an academic conference, we allow scholars to explore a graph of related entities and a word cloud representing the links, providing the user a comprehensive, compact and structured overview about the explored scholar. In this demonstration, the users, as case study are the participants of UMAP'20, will be asked to explore an entity relation graph (profile) of a given participant (who accepted that his profile be presented to others for the matter of this research), and then based on this process, to give feedback about each of the visualization elements and the overall experience. Based on the informative feedbacks elicited, we will be able to evaluate to what extent this visualization would help in giving comprehensive overview in the exploration of any given scholar profile.

References

[1]
Amal, S., Kuflik, T., and Minkov, E. (2017). Harvesting Entity-relation Social Networks from the Web: Potential and Challenges. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP17) pp. 351--352.
[2]
Eades P., Hong S., Nguyen A., Klein K. (2017). Shape-based quality metrics for large graph visualization. J. Graph Algorithms Appl., 21 (1). pp. 29--53.
[3]
Miller, N., Resnick, P. and Zeckhauser, R. Eliciting informative feedback: The peer-prediction method. Management Science, 51(9):1359--1373, 2005.

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  1. Visualizing Personalized Multifaceted ad-hoc Social Network

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    cover image ACM Conferences
    UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
    July 2020
    395 pages
    ISBN:9781450379502
    DOI:10.1145/3386392
    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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    New York, NY, United States

    Publication History

    Published: 13 July 2020

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    Author Tags

    1. entity profiling
    2. graph-based representation
    3. personalized multifaceted graph visualization

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