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  • PhD Defense - Xinran He

    Tue, Apr 18, 2017 @ 01:00 PM - 03:00 PM

    Computer Science

    University Calendar

    Phd Candidate: Xinran He

    Yan Liu (chair)
    David Kempe
    Kristina Lerman
    Thomas Valente

    Date/Time: April 18th 1-3pm

    Room: PHE 223

    Title Understanding Diffusion Processes: Inference and Theory


    Nowadays online social networks have become a ubiquitous tool for people's social communications. Analyzing these social networks offers great potential to shed light on the human social structure, and create better channels to enable social communications and collaborations. While most social analysis tasks begin with extracting or learning the social network and the associated parameters, it remains a very challenging task due to the amorphous nature of social ties and the noise and incompleteness in the observations. As a result, the inferred social network is likely to be of low accuracy and high level of noise which impacts the performance of analysis and applications depending on the inferred parameters.

    In this thesis, we study the following important questions with a special focus on analyzing diffusion behaviors in social networks to achieve real practicality: (1) How to utilize special properties of social networks to improve the 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?

    To address the first challenge towards accurate network inference, we tackle the issue of mitigating the impact of incomplete observations with a focus on learning influence function from incomplete observations. To address the challenge of data scarcity in inferring diffusion networks, we propose a hierarchical graphical model to jointly infer multiple diffusion networks accurately. To utilize the rich content information in cascades, we propose the HawkesTopic model to analyze text-based cascades by combining temporal and content information.

    To address the second challenge towards designing robust Influence Maximization algorithms, we first propose a framework to measure the stability of Influence Maximization with the Perturbation Interval Model to characterize the noise in the inferred diffusion network. We then design an efficient algorithm for Robust Influence Maximization to find influential users robust in multiple diffusion settings.

    Location: Charles Lee Powell Hall (PHE) - 223

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon


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