Skip to main content
Hide Search Box
MIDAS
Open off-canvas menu
MENUMENU
  • About
    • Overview
    • Governance and Advisory Boards
    • Annual Highlights
  • Research
    • Overview
    • Research Accomplishments
    • COVID-19 Research
    • MIDAS Funded Projects
    • Partner Research Centers
      • DATA
      • FIDES
      • PIT-KN
  • Training
    • Graduate Certificate Program
    • Other Programs
    • Courses
    • Experiential Learning
    • Workshops
  • Events
    • Calendar
    • Seminar Series
    • MIDAS Data Science and AI Summer Camp
    • 2022 Data for Public Good Symposium
    • Past Events
  • Partnerships
    • Overview
    • Academic Partnerships
    • Data Science for Social Good
    • Future Leaders Summit
  • People
    • Our Team
    • Faculty
    • Michigan Data Science Fellows
    • Communities of Practice
      • Staff Collective for Data Science
      • NLP+CSS 201
    • Student Community
  • Resources
    • Jobs
    • Grants
    • Reproducibility Hub
    • Research Datasets
    • Newsletters
  • Open Search Box
  • Toggle side widget area
  • No menu assigned!
Sep 12
0

Paramveer Dhillon

By ljing |

My research interests span topics in Statistical Machine Learning, Computational Social Science, Natural Language Processing, and Field/Digital Experiments. Substantively, I am interested in understanding the impact of internet technologies on users by empirically studying their interactions with such systems. The research questions that I study are of both predictive as well as causal nature and I examine them by using data from text/natural language and social network domains.

The same network is displayed, parameterized by four different models of the distribution of influence and susceptibility over nodes, characterized by four types of nodes: low influence and low susceptibility nodes, high influence and low susceptibility nodes, high influence and high susceptibility nodes and low influence and high susceptibility nodes. The optimal seed nodes selected under each model are outlined in green. a, Baseline IC and LT models for which propagation properties are specified as constant (top) and the inverse of node degree (bottom), respectively. b, Baseline IC and LT models for which propagation properties are specified according to the assortative influence, assortative susceptibility, substitute influence–susceptibility (AAS) model. c–e, The same information as in b, but for the assortative influence, disassortative susceptibility, substitute influence–susceptibility (ADS; c), disassortative influence, disassortative susceptibility, substitute influence–susceptibility (DDS; d) and disassortative influence, assortative susceptibility, substitute influence–susceptibility (DAS; e) empirical influence models. Distributions of the frequency of the four types of nodes with different influence and susceptibility characterizations are displayed underneath each graph or model. Seed sets differ substantially across different parameterizations of the graph, implying vastly different influence maximization results for the different models of influence and susceptibility. **This figure is taken from the paper: “Social influence maximization under empirical influence models” by (Aral and Dhillon, Nature Human Behaviour 2018).**

 

MIDAS Faculty Research Pitch, Fall 2021

Sign Up for the MIDAS Newsletter

Contact Us

Visit MIDAS on Twitter

MIDAS is a unit of the Office of Research University of Michigan Office of Research

Copyright © 2020 The Regents of the University of Michigan

  • U-M Gateway
  • U-M Non-discrimination Statement
  • UMOR

High Contrast Styles:

Toggle Stylesheet
Close Side Widget Area
    MENUMENU
    • About
      • Overview
      • Governance and Advisory Boards
      • Annual Highlights
    • Research
      • Overview
      • Research Accomplishments
      • COVID-19 Research
      • MIDAS Funded Projects
      • Partner Research Centers
        • DATA
        • FIDES
        • PIT-KN
    • Training
      • Graduate Certificate Program
      • Other Programs
      • Courses
      • Experiential Learning
      • Workshops
    • Events
      • Calendar
      • Seminar Series
      • MIDAS Data Science and AI Summer Camp
      • 2022 Data for Public Good Symposium
      • Past Events
    • Partnerships
      • Overview
      • Academic Partnerships
      • Data Science for Social Good
      • Future Leaders Summit
    • People
      • Our Team
      • Faculty
      • Michigan Data Science Fellows
      • Communities of Practice
        • Staff Collective for Data Science
        • NLP+CSS 201
      • Student Community
    • Resources
      • Jobs
      • Grants
      • Reproducibility Hub
      • Research Datasets
      • Newsletters