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Understanding Diffusion Processes: Inference and Theory

Published: 08 February 2016 Publication History

Abstract

With increasing popularity of social media and social networks sites, analyzing the social networks offers great potential to shed light on human social structure and provides great marketing opportunities. Usually, social network analysis starts with extracting or learning the social network and the associated parameters. Contrary to other analytical tasks, this step is highly non-trivial due to amorphous nature of social ties and the challenges of noisy and incomplete observations. My research focuses on improving accuracy in inferring the network as well as analyzing the consequences when the extracted network is noisy or erroneous. To be more precise, I propose to study the following two questions with a special focus on analyzing diffusion behaviors: (1) How to utilize special properties of social networks to improve accuracy of the extracted network under noisy and missing data; (2) How to characterize the impact of noise in the inferred network and carry out robust analysis and optimization.

References

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X. He, T. Rekatsinas, J. Foulds, L. Getoor, and Y. Liu. Hawkestopic: A joint model for network inference and topic modeling from text-based cascades. In Proc. 32nd Intl. Conf. on Machine Learning, 2015.
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D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence in a social network. In Proc. 9th Intl. Conf. on Knowledge Discovery and Data Mining, pages 137--146, 2003.
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S. W. Linderman and R. P. Adams. Discovering latent network structure in point process data. In Proc. 31st Intl. Conf. on Machine Learning, 2014.
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S.-H. Yang and H. Zha. Mixture of mutually exciting processes for viral diffusion. In Proc. 30th Intl. Conf. on Machine Learning, 2013.
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K. Zhou, H. Zha, and L. Song. Learning social infectivity in sparse low-rank network using multi-dimensional hawkes processes. In Proc. 30th Intl. Conf. on Machine Learning, 2013.

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  1. Understanding Diffusion Processes: Inference and Theory

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    cover image ACM Conferences
    WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
    February 2016
    746 pages
    ISBN:9781450337168
    DOI:10.1145/2835776
    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: 08 February 2016

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    Author Tags

    1. diffusion process
    2. influence maximization
    3. network inference
    4. social network analysis

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    • DARPA SMISC

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    WSDM 2016
    WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining
    February 22 - 25, 2016
    California, San Francisco, USA

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    WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

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