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VERSION:2.0
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X-WR-CALNAME:Network Science Institute: Configuration Models of Random Hype
 rgraphs and their Applications
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260915T054613Z
UID:tag:localist.com\,2008:EventInstance_4464213
DTSTART:20190326T180000Z
DTEND:20190326T190000Z
DESCRIPTION:Title: Configuration Models of Random Hypergraphs and their App
 licationsSpeaker: Phil Chodrow\, Massachusetts Institute of TechnologyDate
 : Tuesday\, March 26\, 2019Time: 3:00pm - 4:00pmLocation: 11th floor\, 177
  Huntington Ave\, Boston MA\, 02115AbstractNetworks of dyadic relationship
 s between entities have emerged as a dominant paradigm for modeling comple
 x systems. Many empirical "networks”—such as collaboration networks\; 
 co-occurence networks\; and communication networks—are intrinsically pol
 yadic\, with multiple entities interacting simultaneously. Historically\, 
 such polyadic data has been represented dyadically via a standard projecti
 on operation. While convenient\, this projection often has unintended and 
 uncontrolled impact on downstream analysis\, especially null hypothesis-te
 sting. In this work\, we develop a class of random null models for polyadi
 c data in the framework of hypergraphs\, therefore circumventing the need 
 for projection. The null models we define are uniform on the space of hype
 rgraphs sharing common degree and edge dimension sequences\, and thus prov
 ide direct generalizations of the classical configuration model of network
  science. We also derive Metropolis-Hastings algorithms in order to sample
  from these spaces. We then apply the model to study two classical network
  topics—clustering and assortativity—as well as one contemporary\, pol
 yadic topic—simplicial closure. In each application\, we emphasize the i
 mportance of randomizing over hypergraph space rather than projected graph
  space\, showing that this choice can dramatically alter directional study
  conclusions and statistical findings. For example\, we find that many of 
 social networks we study are less clustered than would be expected at rand
 om\, a finding in tension with much conventional wisdom within network sci
 ence. Our findings underscore the importance of carefully choosing appropr
 iate null spaces for polyadic relational data\, and demonstrate the utilit
 y of random hypergraphs in many study contexts. Link to arXiv paper: [http
 s://arxiv.org/abs/1902.09302]About the SpeakerPhil Chodrow is an applied m
 athematician working on methodological problems that arise in the scientif
 ic study of complex social systems. He is a PhD student and a member of MI
 T’s Operations Research Center and the Laboratory for Information and De
 cision Systems. His interests include network inference\; dynamics on netw
 orks\; applied information theory\; and spatial data science. Application 
 areas include opinion dynamics\; demographic segregation\; and parameter e
 stimation in complex dynamics. Link to website: [https://www.philchodrow.c
 om/]
LOCATION:
SUMMARY:Network Science Institute: Configuration Models of Random Hypergrap
 hs and their Applications
URL;VALUE=URI:https://calendar.northeastern.edu/event/network_science_insti
 tute_configuration_models_of_random_hypergraphs_and_their_applications
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